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- From: saswss@unx.sas.com (Warren Sarle)
- Newsgroups: comp.ai.neural-nets,comp.answers,news.answers
- Subject: comp.ai.neural-nets FAQ, Part 7 of 7: Hardware
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-
- Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC,
- USA. Answers provided by other authors as cited below are copyrighted by
- those authors, who by submitting the answers for the FAQ give permission for
- the answer to be reproduced as part of the FAQ in any of the ways specified
- in part 1 of the FAQ.
-
- This is part 7 (of 7) of a monthly posting to the Usenet newsgroup
- comp.ai.neural-nets. See the part 1 of this posting for full information
- what it is all about.
-
- ========== Questions ==========
- ********************************
-
- Part 1: Introduction
- Part 2: Learning
- Part 3: Generalization
- Part 4: Books, data, etc.
- Part 5: Free software
- Part 6: Commercial software
- Part 7: Hardware and miscellaneous
-
- Neural Network hardware?
- What are some applications of NNs?
- General
- Agriculture
- Automotive
- Chemistry
- Criminology
- Face recognition
- Finance and economics
- Games, sports, gambling
- Industry
- Materials science
- Medicine
- Music
- Robotics
- Weather forecasting
- Weird
- What to do with missing/incomplete data?
- How to forecast time series (temporal sequences)?
- How to learn an inverse of a function?
- How to get invariant recognition of images under translation, rotation,
- etc.?
- How to recognize handwritten characters?
- What about pulsed or spiking NNs?
- What about Genetic Algorithms and Evolutionary Computation?
- What about Fuzzy Logic?
- Unanswered FAQs
- Other NN links?
-
- ------------------------------------------------------------------------
-
- Subject: Neural Network hardware?
- =================================
-
- Overview articles:
-
- o Clark S. Lindsey and Thomas Lindblad (1998), "Review of hardware neural
- networks: A user's perspective",
- http://www.particle.kth.se/~lindsey/elba2html/elba2html.html
-
- o P. D. Moerland and E. Fiesler (1997), "Neural Network Adaptations to
- Hardware Implementations", in Handbook of Neural Computation,
- http://www.idiap.ch/~perry/moerland-97.1.bib.abs.html
-
- The journal, IEEE Transactions on Neural Networks, plans to have a
- special issue on neural networks hardware implementations in September,
- 2003.
-
- Various NN hardware information can be found at the following web sites:
-
- o Pacific Northwest National Laboratory:
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/systems/commercial.html
- o Dr. Denise Gorse, University College London:
- http://www.cs.ucl.ac.uk/staff/D.Gorse/research/pRAM.html
- o Neural Chips and Evolvable Hardware:
- http://glendhu.com/ai/neuralchips/
-
- ------------------------------------------------------------------------
-
- Subject: What are some applications of NNs?
- ===========================================
-
- There are vast numbers of published neural network applications. If you
- don't find something from your field of interest below, try a web search.
- Here are some useful search engines:
- http://www.google.com/
- http://search.yahoo.com/
- http://www.altavista.com/
- http://www.deja.com/
-
- General
- -------
-
- o The Pacific Northwest National Laboratory:
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/ including a list of
- commercial applications at
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/products/
- o The Stimulation Initiative for European Neural Applications:
- http://www.mbfys.kun.nl/snn/siena/cases/
- o The DTI NeuroComputing Web's Applications Portfolio:
- http://www.globalweb.co.uk/nctt/portfolo/
- o The Applications Corner, NeuroDimension, Inc.:
- http://www.nd.com/appcornr/purpose.htm
- o The BioComp Systems, Inc. Solutions page: http://www.bio-comp.com
- o Chen, C.H., ed. (1996) Fuzzy Logic and Neural Network Handbook, NY:
- McGraw-Hill, ISBN 0-07-011189-8.
- o The series Advances in Neural Information Processing Systems containing
- proceedings of the conference of the same name, published yearly by
- Morgan Kauffman starting in 1989 and by The MIT Press in 1995.
-
- Agriculture
- -----------
-
- o P.H. Heinemann, Automated Grading of Produce:
- http://server.age.psu.edu/dept/fac/Heinemann/phhdocs/visionres.html
- o Deck, S., C.T. Morrow, P.H. Heinemann, and H.J. Sommer, III. 1995.
- Comparison of a neural network and traditional classifier for machine
- vision inspection. Applied Engineering in Agriculture. 11(2):319-326.
- o Tao, Y., P.H. Heinemann, Z. Varghese, C.T. Morrow, and H.J. Sommer III.
- 1995. Machine vision for color inspection of potatoes and apples.
- Transactions of the American Society of Agricultural Engineers.
- 38(5):1555-1561.
-
- Automotive
- ----------
-
- o "No Hands Across America Journal" - steering a car:
- http://cart.frc.ri.cmu.edu/users/hpm/project.archive/reference.file/Journal.html
- Photos:
- http://www.techfak.uni-bielefeld.de/ags/ti/personen/zhang/seminar/intelligente-autos/tour.html
-
- Chemistry
- ---------
-
- o PNNL, General Applications of Neural Networks in Chemistry and Chemical
- Engineering:
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/chemistry.html.
- o Prof. Dr. Johann Gasteiger, Neural Networks and Genetic Algorithms in
- Chemistry:
- http://www2.ccc.uni-erlangen.de/publications/publ_topics/publ_topics-12.html
- o Roy Goodacre, pyrolysis mass spectrometry:
- http://gepasi.dbs.aber.ac.uk/roy/pymshome.htm and Fourier transform
- infrared (FT-IR) spectroscopy:
- http://gepasi.dbs.aber.ac.uk/roy/ftir/ftirhome.htm contain applications
- of a variety of NNs as well as PLS (partial least squares) and other
- statistical methods.
- o Situs, a program package for the docking of protein crystal structures to
- single-molecule, low-resolution maps from electron microscopy or small
- angle X-ray scattering: http://chemcca10.ucsd.edu/~situs/
- o An on-line application of a Kohonen network with a 2-dimensional output
- layer for prediction of protein secondary structure percentages from UV
- circular dichroism spectra: http://www.embl-heidelberg.de/~andrade/k2d/.
-
- Criminology
- -----------
-
- o Computer Aided Tracking and Characterization of Homicides and Sexual
- Assaults (CATCH):
- http://lancair.emsl.pnl.gov:2080/proj/neuron/papers/kangas.spie99.abs.html
-
- Face recognition
- ----------------
-
- o Face Recognition Home Page: http://www.cs.rug.nl/~peterkr/FACE/face.html
- o Konen, W., "Neural information processing in real-world face-recognition
- applications,"
- http://www.computer.muni.cz/pubs/expert/1996/trends/x4004/konen.htm
- o Jiang, Q., "Principal Component Analysis and Neural Network Based Face
- Recognition," http://people.cs.uchicago.edu/~qingj/ThesisHtml/
- o Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D. (1997), "Face
- Recognition: A Convolutional Neural Network Approach," IEEE Transactions
- on Neural Networks, 8, 98-113,
- http://www.neci.nec.com/~lawrence/papers/face-tnn97/latex.html
-
- Finance and economics
- ---------------------
-
- o Athanasios Episcopos, References on Neural Net Applications to Finance
- and Economics: http://www.compulink.gr/users/episcopo/neurofin.html
- o Franco Busetti, Heuristics and artificial intelligence in finance and
- investment: http://www.geocities.com/francorbusetti/
- o Trippi, R.R. & Turban, E. (1993), Neural Networks in Finance and
- Investing, Chicago: Probus.
- o Zirilli, J.S. (1996), Financial Prediction Using Neural Networks,
- International Thomson Publishing, ISBN 1850322341,
- http://www6.bcity.com/mjfutures/
- o Andreas S. Weigend, Yaser Abu-Mostafa, A. Paul N. Refenes (eds.) (1997)
- Decision Technologies for Financial Engineering: Proceedings of the Fourth
- International Conference on Neural Networks in the Capital Markets (Nncm
- '96) Publisher: World Scientific Publishing Company, ISBN: 9810231245
-
- Games, sports, gambling
- -----------------------
-
- o General:
-
- Jay Scott, Machine Learning in Games:
- http://satirist.org/learn-game/index.html
-
- METAGAME Game-Playing Workbench:
- ftp://ftp.cl.cam.ac.uk/users/bdp/METAGAME
-
- R.S. Sutton, "Learning to predict by the methods of temporal
- differences", Machine Learning 3, p. 9-44 (1988).
-
- David E. Moriarty and Risto Miikkulainen (1994). "Evolving Neural
- Networks to Focus Minimax Search," In Proceedings of Twelfth National
- Conference on Artificial Intelligence (AAAI-94, Seattle, WA), 1371-1377.
- Cambridge, MA: MIT Press,
- http://www.cs.utexas.edu/users/nn/pages/publications/neuro-evolution.html
-
- Games World '99 at http://gamesworld99.free.fr/menuframe.htm
-
- o Backgammon:
-
- G. Tesauro and T.J. Sejnowski (1989), "A Parallel Network that learns to
- play Backgammon," Artificial Intelligence, vol 39, pp. 357-390.
-
- G. Tesauro and T.J. Sejnowski (1990), "Neurogammon: A Neural Network
- Backgammon Program," IJCNN Proceedings, vol 3, pp. 33-39, 1990.
-
- G. Tesauro (1995), "Temporal Difference Learning and TD-Gammon,"
- Communications of the ACM, 38, 58-68,
- http://www.research.ibm.com/massive/tdl.html
-
- Pollack, J.P. and Blair, A.D. (1997), "Co-Evolution in the Successful
- Learning of Backgammon Strategy," Brandeis University Computer Science
- Technical Report CS-97-193,
- http://www.demo.cs.brandeis.edu/papers/long.html#hcgam97
-
- o Bridge:
-
- METAGAME: ftp://ftp.cl.cam.ac.uk/users/bdp/bridge.ps.Z
-
- He Yo, Zhen Xianjun, Ye Yizheng, Li Zhongrong (19??), "Knowledge
- acquisition and reasoning based on neural networks - the research of a
- bridge bidding system," INNC '90, Paris, vol 1, pp. 416-423.
-
- M. Kohle and F. Schonbauer (19??), "Experience gained with a neural
- network that learns to play bridge," Proc. of the 5th Austrian Artificial
- Intelligence meeting, pp. 224-229.
-
- o Checkers/Draughts:
-
- Mark Lynch (1997), "NeuroDraughts: an application of temporal difference
- learning to draughts,"
- http://www.ai.univie.ac.at/~juffi/lig/Papers/lynch-thesis.ps.gz Software
- available at
- http://satirist.org/learn-game/archive/NeuroDraughts-1.00.zip
-
- K. Chellapilla and D. B. Fogel, "Co-Evolving Checkers Playing Programs
- using Only Win, Lose, or Draw," SPIE's AeroSense'99: Applications and
- Science of Computational Intelligence II, Apr. 5-9, 1999, Orlando,
- Florida, USA, http://vision.ucsd.edu/~kchellap/Publications.html
-
- David Fogel (1999), Evolutionary Computation: Toward a New Philosophy
- of Machine Intelligence (2nd edition), IEEE, ISBN: 078035379X
-
- David Fogel (2001), Blondie24: Playing at the Edge of AI, Morgan Kaufmann
- Publishers, ISBN: 1558607838
- According to the publisher, this is:
-
- ... the first book to bring together the most advanced work in the
- general use of evolutionary computation for creative results. It is
- well suited for the general computer science audience.
-
- Here's the story of a computer that taught itself to play checkers
- far better than its creators ever could. Blondie24 uses a program
- that emulates the basic principles of Darwin evolution to discover on
- its own how to excel at the game. Through this entertaining story,
- the book provides the reader some of the history of AI and explores
- its future.
-
- Unlike Deep Blue, the celebrated chess machine that beat Garry
- Kasparov, the former world champion chess player, this evolutionary
- program didn't have access to other games played by human grand
- masters, or databases of moves for the endgame. It created its own
- means for evaluating the patterns of pieces that it experienced by
- evolving artificial neural networks--mathematical models that loosely
- describe how a brain works.
-
- See http://www.natural-selection.com/NSIPublicationsOnline.htm for a variety
- of online papers by Fogel.
-
- Not NNs, but classic papers:
-
- A.L. Samuel (1959), "Some studies in machine learning using the game of
- checkers," IBM journal of Research and Development, vol 3, nr. 3, pp.
- 210-229.
-
- A.L. Samuel (1967), "Some studies in machine learning using the game of
- checkers 2 - recent progress," IBM journal of Research and Development, vol
- 11, nr. 6, pp. 601-616.
-
- o Chess:
-
- Sebastian Thrun, NeuroChess:
- http://satirist.org/learn-game/systems/neurochess.html
-
- Luke Pellen, Octavius: http://home.seol.net.au/luke/octavius/
-
- Louis Savain (AKA Nemesis), Animal, a spiking neural network that the author
- hopes will learn to play a passable game of chess after he implements the
- motivation mechanism:
- http://home1.gte.net/res02khr/AI/Temporal_Intelligence.htm
-
- o Dog racing:
-
- H. Chen, P. Buntin Rinde, L. She, S. Sutjahjo, C. Sommer, D. Neely (1994),
- "Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on
- Greyhound Racing," IEEE Expert, December 1994, 21-27,
- http://ai.bpa.arizona.edu/papers/dog93/dog93.html
-
- o Football (Soccer):
-
- Kuonen Diego, "Statistical Models for Knock-out Soccer Tournaments",
- http://dmawww.epfl.ch/~kuonen/CALCIO/ (not neural nets, but relevant)
-
- o Go:
-
- David Stoutamire (19??), "Machine Learning, Game Play, and Go," Center for
- Automation and Intelligent Systems Research TR 91-128, Case Western Reserve
- University. http://www.stoutamire.com/david/publications.html
-
- David Stoutamire (1991), Machine Learning Applied to Go, M.S. thesis, Case
- Western Reserve University, ftp://ftp.cl.cam.ac.uk/users/bdp/go.ps.Z
-
- Schraudolph, N., Dayan, P., Sejnowski, T. (1994), "Temporal Difference
- Learning of Position Evaluation in the Game of Go," In: Neural Information
- Processing Systems 6, Morgan Kaufmann 1994,
- ftp://bsdserver.ucsf.edu/Go/comp/td-go.ps.Z
-
- P. Donnelly, P. Corr & D. Crookes (1994), "Evolving Go Playing Strategy in
- Neural Networks", AISB Workshop on Evolutionary Computing, Leeds, England,
- ftp://www.joy.ne.jp/welcome/igs/Go/computer/egpsnn.ps.Z or
- ftp://ftp.cs.cuhk.hk/pub/neuro/GO/techreports/egpsnn.ps.Z
-
- Markus Enzenberger (1996), "The Integration of A Priori Knowledge into a Go
- Playing Neural Network,"
- http://www.cgl.ucsf.edu/go/Programs/neurogo-html/neurogo.html
-
- Norman Richards, David Moriarty, and Risto Miikkulainen (1998), "Evolving
- Neural Networks to Play Go," Applied Intelligence, 8, 85-96,
- http://www.cs.utexas.edu/users/nn/pages/publications/neuro-evolution.html
-
- Dahl, F. A. (1999), "Honte, a Go-playing program using neural nets",
- http://www.ai.univie.ac.at/icml-99-ws-games/papers/dahl.ps.gz
-
- o Go-Moku:
-
- Freisleben, B., "Teaching a Neural Network to Play GO-MOKU," in I.
- Aleksander and J. Taylor, eds, Artificial Neural Networks 2, Proc. of
- ICANN-92, Brighton UK, vol. 2, pp. 1659-1662, Elsevier Science Publishers,
- 1992
-
- Katz, W.T. and Pham, S.P. "Experience-Based Learning Experiments using
- Go-moku", Proc. of the 1991 IEEE International Conference on Systems, Man,
- and Cybernetics, 2: 1405-1410, October 1991.
-
- o Olympics:
-
- E.M.Condon, B.L.Golden, E.A.Wasil (1999), "Predicting the success of nations
- at the Summer Olympics using neural networks", Computers & Operations
- Research, 26, 1243-1265.
-
- o Pong:
-
- http:// www.engin.umd.umich.edu/~watta/MM/pong/pong5.html
-
- o Reversi/Othello:
-
- David E. Moriarty and Risto Miikkulainen (1995). Discovering Complex Othello
- Strategies through Evolutionary Neural Networks. Connection Science, 7,
- 195-209,
- http://www.cs.utexas.edu/users/nn/pages/publications/neuro-evolution.html
-
- Yoshioka, T., Ishii, S., and Ito, M., Strategy acquisition for the game
- ``Othello'' based on reinforcement learning, IEICE Transactions on
- Information and Systems E82-D 12, 1618-1626, 1999,
- http://mimi.aist-nara.ac.jp/~taku-y/
-
- o Tic-Tac-Toe/Noughts and Crosses:
-
- Fogel, David Bb (1993), "Using evolutionary programming to construct neural
- networks that are capable of playing tic-tac-toe," Intern. Conf. on Neural
- Networks 1993, IEEE, San Francisco, CA, pp. 875-880.
-
- Richard S. Sutton and Andrew G. Barto (1998), Reinforcement Learning: An
- Introduction The MIT Press, ISBN: 0262193981,
- http://www-anw.cs.umass.edu/~rich/book/the-book.html
-
- Yongzheng Zhang, Chen Teng, Sitan Wei (2000), "Game playing with
- Evolutionary Strategies and Modular Neural Networks: Tic-Tac-Toe,"
- http://www.cs.dal.ca/~mheywood/GAPproject/EvolvingGamePlay.html
-
- Rob Ellison, "Neural Os and Xs,"
- http://www.catfood.demon.co.uk/beta/game.html (An online Javascript demo,
- but you may not live long enough to teach the network to play a mediocre
- game. I'm not sure what kind of network it uses, but maybe you can figure
- that out if you read the source.)
-
- http://listserv.ac.il/~dvorkind/TicTacToe/main_doc.htm, Java classes by Tsvi
- Dvorkind, using reinforcement learning.
-
- Industry
- --------
-
- o PNNL, Neural Network Applications in Manufacturing:
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/manufacturing.html.
- o PNNL, Applications in the Electric Power Industry:
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/power.html.
- o PNNL, Process Control:
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/process.html.
- o Raoul Tawel, Ken Marko, and Lee Feldkamp (1998), "Custom VLSI ASIC for
- Automotive Applications with Recurrent Networks",
- http://www.jpl.nasa.gov/releases/98/ijcnn98.pdf
- o Otsuka, Y. et al. "Neural Networks and Pattern Recognition of Blast
- Furnace Operation Data" Kobelco Technology Review, Oct. 1992, 12
- o Otsuka, Y. et al. "Applications of Neural Network to Iron and Steel
- Making Processes" 2. International Conference on Fuzzy Logic and Neural
- Networks, Iizuka, 1992
- o Staib, W.E. "Neural Network Control System for Electric Arc Furnaces"
- M.P.T. International, 2/1995, 58-61
- o Portmann, N. et al. "Application of Neural Networks in Rolling
- Automation" Iron and Steel Engineer, Feb. 1995, 33-36
- o Gorni, A.A. (2000), "The modelling of hot rolling processes using neural
- networks: A bibliographical review",
- http://www.geocities.com/SiliconValley/5978/neural_1998.html
- o Murat, M. E., and Rudman, A. J., 1992, Automated first arrival picking: A
- neural network approach: Geophysical Prospecting, 40, 587-604.
-
- Materials science
- -----------------
-
- o Phase Transformations Research Group (search for "neural"):
- http://www.msm.cam.ac.uk/phase-trans/pubs/ptpuball.html
-
- Medicine
- --------
-
- o PNNL, Applications in Medicine and Health:
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/bib/medicine.html.
-
- Music
- -----
-
- o Mozer, M. C. (1994), "Neural network music composition by prediction:
- Exploring the benefits of psychophysical constraints and multiscale
- processing," Connection Science, 6, 247-280,
- http://www.cs.colorado.edu/~mozer/papers/music.html.
- o Griffith, N., and Todd, P.M., eds. (1999), Musical Networks: Parallel
- Distributed Perception and Performance, Cambridge, MA: The MIT Press,
- ISBN 0-262-07181-9.
-
- Robotics
- --------
-
- o Institute of Robotics and System Dynamics:
- http://www.robotic.dlr.de/LEARNING/
- o UC Berkeley Robotics and Intelligent Machines Lab:
- http://robotics.eecs.berkeley.edu/
- o Perth Robotics and Automation Laboratory:
- http://telerobot.mech.uwa.edu.au/
- o University of New Hampshire Robot Lab:
- http://www.ece.unh.edu/robots/rbt_home.htm
-
- Weather forecasting and atmospheric science
- -------------------------------------------
-
- o UBC Climate Prediction Group:
- http://www.ocgy.ubc.ca/projects/clim.pred/index.html
- o Artificial Intelligence Research In Environmental Science:
- http://www.salinas.net/~jpeak/airies/airies.html
- o MET-AI, an mailing list for meteorologists and AI researchers:
- http://www.comp.vuw.ac.nz/Research/met-ai
- o Caren Marzban, Ph.D., Research Scientist, National Severe Storms
- Laboratory: http://www.nhn.ou.edu/~marzban/
- o David Myers's references on NNs in atmospheric science:
- http://terra.msrc.sunysb.edu/~dmyers/ai_refs
-
- Weird
- -----
-
- Zaknich, Anthony and Baker, Sue K. (1998), "A real-time system for the
- characterisation of sheep feeding phases from acoustic signals of jaw
- sounds," Australian Journal of Intelligent Information Processing Systems
- (AJIIPS), Vol. 5, No. 2, Winter 1998.
-
- Abstract
- This paper describes a four-channel real-time system for the detection and
- measurement of sheep rumination and mastication time periods by the analysis
- of jaw sounds transmitted through the skull. The system is implemented using
- an 80486 personal computer, a proprietary data acquisition card (PC-126) and
- a custom made variable gain preamplifier and bandpass filter module. Chewing
- sounds are transduced and transmitted to the system using radio microphones
- attached to the top of the sheep heads. The system's main functions are to
- detect and estimate rumination and mastication time periods, to estimate the
- number of chews during the rumination and mastication periods, and to
- provide estimates of the number of boli in the rumination sequences and the
- number of chews per bolus. The individual chews are identified using a
- special energy threshold detector. The rumination and mastication time
- periods are determined by neural network classifier using a combination of
- time and frequency domain features extracted from successive 10 second
- acoustic signal blocks.
-
- ------------------------------------------------------------------------
-
- Subject: What to do with missing/incomplete data?
- ==================================================
-
- The problem of missing data is very complex.
-
- For unsupervised learning, conventional statistical methods for missing data
- are often appropriate (Little and Rubin, 1987; Schafer, 1997; Schafer and
- Olsen, 1998). There is a concise introduction to these methods in the
- University of Texas statistics FAQ at
- http://www.utexas.edu/cc/faqs/stat/general/gen25.html.
-
- For supervised learning, the considerations are somewhat different, as
- discussed by Sarle (1998). The statistical literature on missing data deals
- almost exclusively with training rather than prediction (e.g., Little,
- 1992). For example, if you have only a small proportion of cases with
- missing data, you can simply throw those cases out for purposes of training;
- if you want to make predictions for cases with missing inputs, you don't
- have the option of throwing those cases out! In theory, Bayesian methods
- take care of everything, but a full Bayesian analysis is practical only with
- special models (such as multivariate normal distributions) or small sample
- sizes. The neural net literature contains a few good papers that cover
- prediction with missing inputs (e.g., Ghahramani and Jordan, 1997; Tresp,
- Neuneier, and Ahmad 1995), but much research remains to be done.
-
- References:
-
- Donner, A. (1982), "The relative effectiveness of procedures commonly
- used in multiple regression analysis for dealing with missing values,"
- American Statistician, 36, 378-381.
-
- Ghahramani, Z. and Jordan, M.I. (1994), "Supervised learning from
- incomplete data via an EM approach," in Cowan, J.D., Tesauro, G., and
- Alspector, J. (eds.) Advances in Neural Information Processing Systems
- 6, San Mateo, CA: Morgan Kaufman, pp. 120-127.
-
- Ghahramani, Z. and Jordan, M.I. (1997), "Mixture models for Learning from
- incomplete data," in Greiner, R., Petsche, T., and Hanson, S.J. (eds.)
- Computational Learning Theory and Natural Learning Systems, Volume IV:
- Making Learning Systems Practical, Cambridge, MA: The MIT Press, pp.
- 67-85.
-
- Jones, M.P. (1996), "Indicator and stratification methods for missing
- explanatory variables in multiple linear regression," J. of the American
- Statistical Association, 91, 222-230.
-
- Little, R.J.A. (1992), "Regression with missing X's: A review," J. of the
- American Statistical Association, 87, 1227-1237.
-
- Little, R.J.A. and Rubin, D.B. (1987), Statistical Analysis with Missing
- Data, NY: Wiley.
-
- McLachlan, G.J. (1992) Discriminant Analysis and Statistical Pattern
- Recognition, Wiley.
-
- Sarle, W.S. (1998), "Prediction with Missing Inputs," in Wang, P.P.
- (ed.), JCIS '98 Proceedings, Vol II, Research Triangle Park, NC, 399-402,
- ftp://ftp.sas.com/pub/neural/JCIS98.ps.
-
- Schafer, J.L. (1997), Analysis of Incomplete Multivariate Data, London:
- Chapman & Hall, ISBN 0 412 04061 1.
-
- Schafer, J.L., and Olsen, M.K. (1998), "Multiple imputation for
- multivariate missing-data problems: A data analyst's perspective,"
- http://www.stat.psu.edu/~jls/mbr.pdf or
- http://www.stat.psu.edu/~jls/mbr.ps
-
- Tresp, V., Ahmad, S. and Neuneier, R., (1994), "Training neural networks
- with deficient data", in Cowan, J.D., Tesauro, G., and Alspector, J.
- (eds.) Advances in Neural Information Processing Systems 6, San Mateo,
- CA: Morgan Kaufman, pp. 128-135.
-
- Tresp, V., Neuneier, R., and Ahmad, S. (1995), "Efficient methods for
- dealing with missing data in supervised learning", in Tesauro, G.,
- Touretzky, D.S., and Leen, T.K. (eds.) Advances in Neural Information
- Processing Systems 7, Cambridge, MA: The MIT Press, pp. 689-696.
-
- ------------------------------------------------------------------------
-
- Subject: How to forecast time series (temporal sequences)?
- ==========================================================
-
- In most of this FAQ, it is assumed that the training cases are statistically
- independent. That is, the training cases consist of pairs of input and
- target vectors, (X_i,Y_i), i=1,...,N, such that the conditional
- distribution of Y_i given all the other training data, (X_j,
- j=1,...,N, and Y_j, j=1,...i-1,i+1,...N) is equal to the
- conditional distribution of Y_i given X_i regardless of the values in the
- other training cases. Independence of cases is often achieved by random
- sampling.
-
- The most common violation of the independence assumption occurs when cases
- are observed in a certain order relating to time or space. That is, case
- (X_i,Y_i) corresponds to time T_i, with T_1 < T_2 < ... <
- T_N. It is assumed that the current target Y_i may depend not only on
- X_i but also on (X_i,Y_i) in the recent past. If the T_i are equally
- spaced, the simplest way to deal with this dependence is to include
- additional inputs (called lagged variables, shift registers, or a tapped
- delay line) in the network. Thus, for target Y_i, the inputs may include
- X_i, Y_{i-1}, X_{i-1}, Y_{i-1}, X_{i-2}, etc. (In some
- situations, X_i would not be known at the time you are trying to forecast
- Y_i and would therefore be excluded from the inputs.) Then you can train
- an ordinary feedforward network with these targets and lagged variables. The
- use of lagged variables has been extensively studied in the statistical and
- econometric literature (Judge, Griffiths, Hill, Lⁿtkepohl and Lee, 1985). A
- network in which the only inputs are lagged target values is called an
- "autoregressive model." The input space that includes all of the lagged
- variables is called the "embedding space."
-
- If the T_i are not equally spaced, everything gets much more complicated.
- One approach is to use a smoothing technique to interpolate points at
- equally spaced intervals, and then use the interpolated values for training
- instead of the original data.
-
- Use of lagged variables increases the number of decisions that must be made
- during training, since you must consider which lags to include in the
- network, as well as which input variables, how many hidden units, etc.
- Neural network researchers have therefore attempted to use partially
- recurrent networks instead of feedforward networks with lags (Weigend and
- Gershenfeld, 1994). Recurrent networks store information about past values
- in the network itself. There are many different kinds of recurrent
- architectures (Hertz, Krogh, and Palmer 1991; Mozer, 1994; Horne and Giles,
- 1995; Kremer, 199?). For example, in time-delay neural networks (Lang,
- Waibel, and Hinton 1990), the outputs for predicting target Y_{i-1} are
- used as inputs when processing target Y_i. Jordan networks (Jordan, 1986)
- are similar to time-delay neural networks except that the feedback is an
- exponential smooth of the sequence of output values. In Elman networks
- (Elman, 1990), the hidden unit activations that occur when processing target
- Y_{i-1} are used as inputs when processing target Y_i.
-
- However, there are some problems that cannot be dealt with via recurrent
- networks alone. For example, many time series exhibit trend, meaning that
- the target values tend to go up over time, or that the target values tend to
- go down over time. For example, stock prices and many other financial
- variables usually go up. If today's price is higher than all previous
- prices, and you try to forecast tomorrow's price using today's price as a
- lagged input, you are extrapolating, and extrapolating is unreliable. The
- simplest methods for handling trend are:
-
- o First fit a linear regression predicting the target values from the time,
- Y_i = a + b T_i + noise, where a and b are regression
- weights. Compute residuals R_i = Y_i - (a + b T_i). Then
- train the network using R_i for the target and lagged values. This
- method is rather crude but may work for deterministic linear trends. Of
- course, for nonlinear trends, you would need to fit a nonlinear
- regression.
-
- o Instead of using Y_i as a target, use D_i = Y_i - Y_{i-1} for
- the target and lagged values. This is called differencing and is the
- standard statistical method for handling nondeterministic (stochastic)
- trends. Sometimes it is necessary to compute differences of differences.
-
- For an elementary discussion of trend and various other practical problems
- in forecasting time series with NNs, such as seasonality, see Masters
- (1993). For a more advanced discussion of NN forecasting of economic series,
- see Moody (1998).
-
- There are several different ways to compute forecasts. For simplicity, let's
- assume you have a simple time series, Y_1, ..., Y_99, you want to
- forecast future values Y_f for f > 99, and you decide to use three
- lagged values as inputs. The possibilities include:
-
- Single-step, one-step-ahead, or open-loop forecasting:
- Train a network with target Y_i and inputs Y_{i-1}, Y_{i-2},
- and Y_{i-3}. Let the scalar function computed by the network be
- designated as Net(.,.,.) taking the three input values as arguments
- and returning the output (predicted) value. Then:
- forecast Y_100 as Net(Y_99,Y_98,Y_97)
- forecast Y_101 as Net(Y_100,Y_99,Y_98)
- forecast Y_102 as Net(Y_101,Y_100,Y_99)
- forecast Y_103 as Net(Y_102,Y_101,Y_100)
- forecast Y_104 as Net(Y_103,Y_102,Y_101)
- and so on.
-
- Multi-step or closed-loop forecasting:
- Train the network as above, but:
- forecast Y_100 as P_100 = Net(Y_99,Y_98,Y_97)
- forecast Y_101 as P_101 = Net(P_100,Y_99,Y_98)
- forecast Y_102 as P_102 = Net(P_101,P_100,Y_99)
- forecast Y_103 as P_103 = Net(P_102,P_101,P_100)
- forecast Y_104 as P_104 = Net(P_103,P_102,P_101)
- and so on.
-
- N-step-ahead forecasting:
- For, say, N=3, train the network as above, but:
- compute P_100 = Net(Y_99,Y_98,Y_97)
- compute P_101 = Net(P_100,Y_99,Y_98)
- forecast Y_102 as P_102 = Net(P_101,P_100,Y_99)
- forecast Y_103 as P_103 = Net(P_102,P_101,Y_100)
- forecast Y_104 as P_104 = Net(P_103,P_102,Y_101)
- and so on.
-
- Direct simultaneous long-term forecasting:
- Train a network with multiple targets Y_i, Y_{i+1}, and Y_{i+2}
- and inputs Y_{i-1}, Y_{i-2}, and Y_{i-3}. Let the vector
- function computed by the network be designated as Net3(.,.,.),
- taking the three input values as arguments and returning the output
- (predicted) vector. Then:
- forecast (Y_100,Y_101,Y_102) as Net3(Y_99,Y_98,Y_97)
-
- Which method you choose for computing forecasts will obviously depend in
- part on the requirements of your application. If you have yearly sales
- figures through 1999 and you need to forecast sales in 2003, you clearly
- can't use single-step forecasting. If you need to compute forecasts at a
- thousand different future times, using direct simultaneous long-term
- forecasting would require an extremely large network.
-
- If a time series is a random walk, a well-trained network will predict Y_i
- by simply outputting Y_{i-1}. If you make a plot showing both the target
- values and the outputs, the two curves will almost coincide, except for
- being offset by one time step. People often mistakenly intrepret such a plot
- to indicate good forecasting accuracy, whereas in fact the network is
- virtually useless. In such situations, it is more enlightening to plot
- multi-step forecasts or N-step-ahead forecasts.
-
- For general information on time-series forecasting, see the following URLs:
-
- o Forecasting FAQs: http://forecasting.cwru.edu/faqs.html
- o Forecasting Principles: http://hops.wharton.upenn.edu/forecast/
- o Investment forecasts for stocks and mutual funds:
- http://www.coe.uncc.edu/~hphillip/
-
- References:
-
- Elman, J.L. (1990), "Finding structure in time," Cognitive Science, 14,
- 179-211.
-
- Hertz, J., Krogh, A., and Palmer, R. (1991). Introduction to the Theory of
- Neural Computation. Addison-Wesley: Redwood City, California.
-
- Horne, B. G. and Giles, C. L. (1995), "An experimental comparison of
- recurrent neural networks," In Tesauro, G., Touretzky, D., and Leen, T.,
- editors, Advances in Neural Information Processing Systems 7, pp.
- 697-704. The MIT Press.
-
- Jordan, M. I. (1986), "Attractor dynamics and parallelism in a
- connectionist sequential machine," In Proceedings of the Eighth Annual
- conference of the Cognitive Science Society, pages 531-546. Lawrence
- Erlbaum.
-
- Judge, G.G., Griffiths, W.E., Hill, R.C., Lⁿtkepohl, H., and Lee, T.-C.
- (1985), The Theory and Practice of Econometrics, NY: John Wiley & Sons.
-
- Kremer, S.C. (199?), "Spatio-temporal Connectionist Networks: A Taxonomy
- and Review,"
- http://hebb.cis.uoguelph.ca/~skremer/Teaching/27642/dynamic2/review.html.
-
- Lang, K. J., Waibel, A. H., and Hinton, G. (1990), "A time-delay neural
- network architecture for isolated word recognition," Neural Networks, 3,
- 23-44.
-
- Masters, T. (1993). Practical Neural Network Recipes in C++, San Diego:
- Academic Press.
-
- Moody, J. (1998), "Forecasting the economy with neural nets: A survey of
- challenges and solutions," in Orr, G,B., and Mueller, K-R, eds., Neural
- Networks: Tricks of the Trade, Berlin: Springer.
-
- Mozer, M.C. (1994), "Neural net architectures for temporal sequence
- processing," in Weigend, A.S. and Gershenfeld, N.A., eds. (1994) Time
- Series Prediction: Forecasting the Future and Understanding the Past,
- Reading, MA: Addison-Wesley, 243-264,
- http://www.cs.colorado.edu/~mozer/papers/timeseries.html.
-
- Weigend, A.S. and Gershenfeld, N.A., eds. (1994) Time Series Prediction:
- Forecasting the Future and Understanding the Past, Reading, MA:
- Addison-Wesley.
-
- ------------------------------------------------------------------------
-
- Subject: How to learn an inverse of a function?
- ================================================
-
- Ordinarily, NNs learn a function Y = f(X), where Y is a vector of
- outputs, X is a vector of inputs, and f() is the function to be learned.
- Sometimes, however, you may want to learn an inverse of a function f(),
- that is, given Y, you want to be able to find an X such that Y = f(X).
- In general, there may be many different Xs that satisfy the equation Y =
- f(X).
-
- For example, in robotics (DeMers and Kreutz-Delgado, 1996, 1997), X might
- describe the positions of the joints in a robot's arm, while Y would
- describe the location of the robot's hand. There are simple formulas to
- compute the location of the hand given the positions of the joints, called
- the "forward kinematics" problem. But there is no simple formula for the
- "inverse kinematics" problem to compute positions of the joints that yield a
- given location for the hand. Furthermore, if the arm has several joints,
- there will usually be many different positions of the joints that yield the
- same location of the hand, so the forward kinematics function is many-to-one
- and has no unique inverse. Picking any X such that Y = f(X) is OK if
- the only aim is to position the hand at Y. However if the aim is to
- generate a series of points to move the hand through an arc this may be
- insufficient. In this case the series of Xs need to be in the same "branch"
- of the function space. Care must be taken to avoid solutions that yield
- inefficient or impossible movements of the arm.
-
- As another example, consider an industrial process in which X represents
- settings of control variables imposed by an operator, and Y represents
- measurements of the product of the industrial process. The function Y =
- f(X) can be learned by a NN using conventional training methods. But the
- goal of the analysis may be to find control settings X that yield a product
- with specified measurements Y, in which case an inverse of f(X) is
- required. In industrial applications, financial considerations are
- important, so not just any setting X that yields the desired result Y may
- be acceptable. Perhaps a function can be specified that gives the cost of X
- resulting from energy consumption, raw materials, etc., in which case you
- would want to find the X that minimizes the cost function while satisfying
- the equation Y = f(X).
-
- The obvious way to try to learn an inverse function is to generate a set of
- training data from a given forward function, but designate Y as the input
- and X as the output when training the network. Using a least-squares error
- function, this approach will fail if f() is many-to-one. The problem is
- that for an input Y, the net will not learn any single X such that Y =
- f(X), but will instead learn the arithmetic mean of all the Xs in the
- training set that satisfy the equation (Bishop, 1995, pp. 207-208). One
- solution to this difficulty is to construct a network that learns a mixture
- approximation to the conditional distribution of X given Y (Bishop, 1995,
- pp. 212-221). However, the mixture method will not work well in general for
- an X vector that is more than one-dimensional, such as Y = X_1^2 +
- X_2^2, since the number of mixture components required may increase
- exponentially with the dimensionality of X. And you are still left with the
- problem of extracting a single output vector from the mixture distribution,
- which is nontrivial if the mixture components overlap considerably. Another
- solution is to use a highly robust error function, such as a redescending
- M-estimator, that learns a single mode of the conditional distribution
- instead of learning the mean (Huber, 1981; Rohwer and van der Rest 1996).
- Additional regularization terms or constraints may be required to persuade
- the network to choose appropriately among several modes, and there may be
- severe problems with local optima.
-
- Another approach is to train a network to learn the forward mapping f()
- and then numerically invert the function. Finding X such that Y = f(X)
- is simply a matter of solving a nonlinear system of equations, for which
- many algorithms can be found in the numerical analysis literature (Dennis
- and Schnabel 1983). One way to solve nonlinear equations is turn the problem
- into an optimization problem by minimizing sum(Y_i-f(X_i))^2. This
- method fits in nicely with the usual gradient-descent methods for training
- NNs (Kindermann and Linden 1990). Since the nonlinear equations will
- generally have multiple solutions, there may be severe problems with local
- optima, especially if some solutions are considered more desirable than
- others. You can deal with multiple solutions by inventing some objective
- function that measures the goodness of different solutions, and optimizing
- this objective function under the nonlinear constraint Y = f(X) using
- any of numerous algorithms for nonlinear programming (NLP; see Bertsekas,
- 1995, and other references under "What are conjugate gradients,
- Levenberg-Marquardt, etc.?") The power and flexibility of the nonlinear
- programming approach are offset by possibly high computational demands.
-
- If the forward mapping f() is obtained by training a network, there will
- generally be some error in the network's outputs. The magnitude of this
- error can be difficult to estimate. The process of inverting a network can
- propagate this error, so the results should be checked carefully for
- validity and numerical stability. Some training methods can produce not just
- a point output but also a prediction interval (Bishop, 1995; White, 1992).
- You can take advantage of prediction intervals when inverting a network by
- using NLP methods. For example, you could try to find an X that minimizes
- the width of the prediction interval under the constraint that the equation
- Y = f(X) is satisfied. Or instead of requiring Y = f(X) be satisfied
- exactly, you could try to find an X such that the prediction interval is
- contained within some specified interval while minimizing some cost
- function.
-
- For more mathematics concerning the inverse-function problem, as well as
- some interesting methods involving self-organizing maps, see DeMers and
- Kreutz-Delgado (1996, 1997). For NNs that are relatively easy to invert, see
- the Adaptive Logic Networks described in the software sections of the FAQ.
-
- References:
-
- Bertsekas, D. P. (1995), Nonlinear Programming, Belmont, MA: Athena
- Scientific.
-
- Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford:
- Oxford University Press.
-
- DeMers, D., and Kreutz-Delgado, K. (1996), "Canonical Parameterization of
- Excess motor degrees of freedom with self organizing maps", IEEE Trans
- Neural Networks, 7, 43-55.
-
- DeMers, D., and Kreutz-Delgado, K. (1997), "Inverse kinematics of
- dextrous manipulators," in Omidvar, O., and van der Smagt, P., (eds.)
- Neural Systems for Robotics, San Diego: Academic Press, pp. 75-116.
-
- Dennis, J.E. and Schnabel, R.B. (1983) Numerical Methods for
- Unconstrained Optimization and Nonlinear Equations, Prentice-Hall
-
- Huber, P.J. (1981), Robust Statistics, NY: Wiley.
-
- Kindermann, J., and Linden, A. (1990), "Inversion of Neural Networks by
- Gradient Descent," Parallel Computing, 14, 277-286,
- ftp://icsi.Berkeley.EDU/pub/ai/linden/KindermannLinden.IEEE92.ps.Z
-
- Rohwer, R., and van der Rest, J.C. (1996), "Minimum description length,
- regularization, and multimodal data," Neural Computation, 8, 595-609.
-
- White, H. (1992), "Nonparametric Estimation of Conditional Quantiles
- Using Neural Networks," in Page, C. and Le Page, R. (eds.), Proceedings
- of the 23rd Sympsium on the Interface: Computing Science and Statistics,
- Alexandria, VA: American Statistical Association, pp. 190-199.
-
- ------------------------------------------------------------------------
-
- Subject: How to get invariant recognition of images under
- =========================================================
- translation, rotation, etc.?
- ============================
-
- See:
-
- Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford:
- Oxford University Press, section 8.7.
-
- Masters, T. (1994), Signal and Image Processing with Neural Networks: A
- C++ Sourcebook, NY: Wiley.
-
- Soucek, B., and The IRIS Group (1992), Fast Learning and Invariant Object
- Recognition, NY: Wiley.
-
- Squire, D. (1997), Model-Based Neural Networks for Invariant Pattern
- Recognition, http://cuiwww.unige.ch/~squire/publications.html
-
- Laurenz Wiskott, bibliography on "Unsupervised Learning of Invariances in
- Neural Systems"
- http://www.cnl.salk.edu/~wiskott/Bibliographies/LearningInvariances.html
-
- ------------------------------------------------------------------------
-
- Subject: How to recognize handwritten characters?
- =================================================
-
- URLS:
-
- o Don Tveter's The Pattern Recognition Basis of AI at
- http://www.dontveter.com/basisofai/char.html
- o Andras Kornai's homepage at http://www.cs.rice.edu/~andras/
- o Yann LeCun's homepage at http://www.research.att.com/~yann/
- Data sets of handwritten digits can be found at
- http://www.research.att.com/~yann/exdb/mnist/
-
- Other references:
-
- Hastie, T., and Simard, P.Y. (1998), "Metrics and models for handwritten
- character recognition," Statistical Science, 13, 54-65.
-
- Jackel, L.D. et al., (1994) "Comparison of Classifier Methods: A Case
- Study in Handwritten Digit Recognition", 1994 International Conference on
- Pattern Recognition, Jerusalem
-
- LeCun, Y., Jackel, L.D., Bottou, L., Brunot, A., Cortes, C., Denker,
- J.S., Drucker, H., Guyon, I., Muller, U.A., Sackinger, E., Simard, P.,
- and Vapnik, V. (1995), "Comparison of learning algorithms for handwritten
- digit recognition," in F. Fogelman and P. Gallinari, eds., International
- Conference on Artificial Neural Networks, pages 53-60, Paris.
-
- Orr, G.B., and Mueller, K.-R., eds. (1998), Neural Networks: Tricks of
- the Trade, Berlin: Springer, ISBN 3-540-65311-2.
-
- ------------------------------------------------------------------------
-
- Subject: What about pulsed or spiking NNs?
- ==========================================
-
- The standard reference is:
-
- Maass, W., and Bishop, C.M., eds. (1999) Pulsed Neural Networks,
- Cambridge, MA: The MIT Press, ISBN: 0262133504.
-
- For more information on this book, see the section on "Pulsed/Spiking
- networks" under "Other notable books" in part 4 of the FAQ. Also see
- Professor Maass's web page at http://www.igi.tugraz.at/maass/.
-
- Some other interesting URLs include:
-
- o Laboratory of Computational Neuroscience (LCN) at the Swiss Federal
- Institute of Technology Lausanne,
- http://diwww.epfl.ch/mantra/mantra_bioneuro.html
-
- o The notoriously hyped Berger-Liaw Neural Network Speaker-Independent
- Speech Recognition System,
- http://www.usc.edu/ext-relations/news_service/releases/stories/36013.html
-
- ------------------------------------------------------------------------
-
- Subject: What about Genetic Algorithms?
- =======================================
-
- There are a number of definitions of GA (Genetic Algorithm). A possible one
- is
-
- A GA is an optimization program
- that starts with
- a population of encoded procedures, (Creation of Life :-> )
- mutates them stochastically, (Get cancer or so :-> )
- and uses a selection process (Darwinism)
- to prefer the mutants with high fitness
- and perhaps a recombination process (Make babies :-> )
- to combine properties of (preferably) the succesful mutants.
-
- Genetic algorithms are just a special case of the more general idea of
- "evolutionary computation". There is a newsgroup that is dedicated to the
- field of evolutionary computation called comp.ai.genetic. It has a detailed
- FAQ posting which, for instance, explains the terms "Genetic Algorithm",
- "Evolutionary Programming", "Evolution Strategy", "Classifier System", and
- "Genetic Programming". That FAQ also contains lots of pointers to relevant
- literature, software, other sources of information, et cetera et cetera.
- Please see the comp.ai.genetic FAQ for further information.
-
- For an entertaining introduction to evolutionary training of neural nets,
- see:
-
- David Fogel (2001), Blondie24: Playing at the Edge of AI, Morgan Kaufmann
- Publishers, ISBN: 1558607838
-
- There are other books and papers by Fogel and his colleagues listed under
- "Checkers/Draughts" in the "Games, sports, gambling" section above.
-
- For an extensive review, see:
-
- Yao, X. (1999), "Evolving Artificial Neural Networks," Proceedings of the
- IEEE, 87, 1423-1447, http://www.cs.bham.ac.uk/~xin/journal_papers.html
-
- Here are some other on-line papers about evolutionary training of NNs:
-
- o Backprop+GA: http://geneura.ugr.es/~pedro/G-Prop.htm
-
- o LVQ+GA: http://geneura.ugr.es/g-lvq/g-lvq.html
-
- o Very long chromosomes:
- ftp://archive.cis.ohio-state.edu/pub/neuroprose/korning.nnga.ps.Z
-
- More URLs on genetic algorithms and NNs:
-
- o Omri Weisman and Ziv Pollack's web page on "Neural Network Using Genetic
- Algorithms" at http://www.cs.bgu.ac.il/~omri/NNUGA/
-
- o Christoph M. Friedrich's web page on Evolutionary algorithms and
- Artificial Neural Networks has a bibloigraphy and links to researchers at
- http://www.tussy.uni-wh.de/~chris/gann/gann.html
-
- o Andrew Gray's Hybrid Systems FAQ at the University of Otago at
- http://divcom.otago.ac.nz:800/COM/INFOSCI/SMRL/people/andrew/publications/faq/hybrid/hybrid.htm
-
- o Differential Evolution: http://www.icsi.berkeley.edu/~storn/code.html
-
- For general information on GAs, try the links at
- http://www.shef.ac.uk/~gaipp/galinks.html and http://www.cs.unibo.it/~gaioni
-
- ------------------------------------------------------------------------
-
- Subject: What about Fuzzy Logic?
- ================================
-
- Fuzzy logic is an area of research based on the work of L.A. Zadeh. It is a
- departure from classical two-valued sets and logic, that uses "soft"
- linguistic (e.g. large, hot, tall) system variables and a continuous range
- of truth values in the interval [0,1], rather than strict binary (True or
- False) decisions and assignments.
-
- Fuzzy logic is used where a system is difficult to model exactly (but an
- inexact model is available), is controlled by a human operator or expert, or
- where ambiguity or vagueness is common. A typical fuzzy system consists of a
- rule base, membership functions, and an inference procedure.
-
- Most fuzzy logic discussion takes place in the newsgroup comp.ai.fuzzy
- (where there is a fuzzy logic FAQ) but there is also some work (and
- discussion) about combining fuzzy logic with neural network approaches in
- comp.ai.neural-nets.
-
- Early work combining neural nets and fuzzy methods used competitive networks
- to generate rules for fuzzy systems (Kosko 1992). This approach is sort of a
- crude version of bidirectional counterpropagation (Hecht-Nielsen 1990) and
- suffers from the same deficiencies. More recent work (Brown and Harris 1994;
- Kosko 1997) has been based on the realization that a fuzzy system is a
- nonlinear mapping from an input space to an output space that can be
- parameterized in various ways and therefore can be adapted to data using the
- usual neural training methods (see "What is backprop?") or conventional
- numerical optimization algorithms (see "What are conjugate gradients,
- Levenberg-Marquardt, etc.?").
-
- A neural net can incorporate fuzziness in various ways:
-
- o The inputs can be fuzzy. Any garden-variety backprop net is fuzzy in this
- sense, and it seems rather silly to call a net "fuzzy" solely on this
- basis, although Fuzzy ART (Carpenter and Grossberg 1996) has no other
- fuzzy characteristics.
- o The outputs can be fuzzy. Again, any garden-variety backprop net is fuzzy
- in this sense. But competitive learning nets ordinarily produce crisp
- outputs, so for competitive learning methods, having fuzzy output is a
- meaningful distinction. For example, fuzzy c-means clustering (Bezdek
- 1981) is meaningfully different from (crisp) k-means. Fuzzy ART does not
- have fuzzy outputs.
- o The net can be interpretable as an adaptive fuzzy system. For example,
- Gaussian RBF nets and B-spline regression models (Dierckx 1995, van
- Rijckevorsal 1988) are fuzzy systems with adaptive weights (Brown and
- Harris 1994) and can legitimately be called neurofuzzy systems.
- o The net can be a conventional NN architecture that operates on fuzzy
- numbers instead of real numbers (Lippe, Feuring and Mischke 1995).
- o Fuzzy constraints can provide external knowledge (Lampinen and Selonen
- 1996).
-
- More information on neurofuzzy systems is available online:
-
- o The Fuzzy Logic and Neurofuzzy Resources page of the Image, Speech and
- Intelligent Systems (ISIS) research group at the University of
- Southampton, Southampton, Hampshire, UK:
- http://www-isis.ecs.soton.ac.uk/research/nfinfo/fuzzy.html.
- o The Neuro-Fuzzy Systems Research Group's web page at Tampere University
- of Technology, Tampere, Finland: http://www.cs.tut.fi/~tpo/group.html and
- http://dmiwww.cs.tut.fi/nfs/Welcome_uk.html
- o Marcello Chiaberge's Neuro-Fuzzy page at
- http://polimage.polito.it/~marcello.
- o The homepage of the research group on Neural Networks and Fuzzy Systems
- at the Institute of Knowledge Processing and Language Engineering,
- Faculty of Computer Science, University of Magdeburg, Germany, at
- http://www.neuro-fuzzy.de/
- o Jyh-Shing Roger Jang's home page at http://www.cs.nthu.edu.tw/~jang/ with
- information on ANFIS (Adaptive Neuro-Fuzzy Inference Systems), articles
- on neuro-fuzzy systems, and more links.
- o Andrew Gray's Hybrid Systems FAQ at the University of Otago at
- http://divcom.otago.ac.nz:800/COM/INFOSCI/SMRL/people/andrew/publications/faq/hybrid/hybrid.htm
-
- References:
-
- Bezdek, J.C. (1981), Pattern Recognition with Fuzzy Objective Function
- Algorithms, New York: Plenum Press.
-
- Bezdek, J.C. & Pal, S.K., eds. (1992), Fuzzy Models for Pattern
- Recognition, New York: IEEE Press.
-
- Brown, M., and Harris, C. (1994), Neurofuzzy Adaptive Modelling and
- Control, NY: Prentice Hall.
-
- Carpenter, G.A. and Grossberg, S. (1996), "Learning, Categorization, Rule
- Formation, and Prediction by Fuzzy Neural Networks," in Chen, C.H.
- (1996), pp. 1.3-1.45.
-
- Chen, C.H., ed. (1996) Fuzzy Logic and Neural Network Handbook, NY:
- McGraw-Hill, ISBN 0-07-011189-8.
-
- Dierckx, P. (1995), Curve and Surface Fitting with Splines, Oxford:
- Clarendon Press.
-
- Hecht-Nielsen, R. (1990), Neurocomputing, Reading, MA: Addison-Wesley.
-
- Klir, G.J. and Folger, T.A.(1988), Fuzzy Sets, Uncertainty, and
- Information, Englewood Cliffs, N.J.: Prentice-Hall.
-
- Kosko, B.(1992), Neural Networks and Fuzzy Systems, Englewood Cliffs,
- N.J.: Prentice-Hall.
-
- Kosko, B. (1997), Fuzzy Engineering, NY: Prentice Hall.
-
- Lampinen, J and Selonen, A. (1996), "Using Background Knowledge for
- Regularization of Multilayer Perceptron Learning", Submitted to
- International Conference on Artificial Neural Networks, ICANN'96, Bochum,
- Germany.
-
- Lippe, W.-M., Feuring, Th. and Mischke, L. (1995), "Supervised learning
- in fuzzy neural networks," Institutsbericht Angewandte Mathematik und
- Informatik, WWU Muenster, I-12,
- http://wwwmath.uni-muenster.de/~feuring/WWW_literatur/bericht12_95.ps.gz
-
- Nauck, D., Klawonn, F., and Kruse, R. (1997), Foundations of
- Neuro-Fuzzy Systems, Chichester: Wiley, ISBN 0-471-97151-0.
-
- van Rijckevorsal, J.L.A. (1988), "Fuzzy coding and B-splines," in van
- Rijckevorsal, J.L.A., and de Leeuw, J., eds., Component and
- Correspondence Analysis, Chichester: John Wiley & Sons, pp. 33-54.
-
- ------------------------------------------------------------------------
-
- Subject: Unanswered FAQs
- ========================
-
- o How many training cases do I need?
- o How should I split the data into training and validation sets?
- o What error functions can be used?
- o How can I select important input variables?
- o Should NNs be used in safety-critical applications?
-
- ------------------------------------------------------------------------
-
- Subject: Other NN links?
- ========================
-
- o Search engines
- o ++++++++++++++
-
- o Yahoo:
- http://www.yahoo.com/Science/Engineering/Electrical_Engineering/Neural_Networks/
- o Neuroscience Web Search: http://www.acsiom.org/nsr/neuro.html
-
- o Archives of NN articles and software
- o ++++++++++++++++++++++++++++++++++++
-
- o Neuroprose ftp archive site
- o ---------------------------
-
- ftp://archive.cis.ohio-state.edu/pub/neuroprose This directory
- contains technical reports as a public service to the connectionist
- and neural network scientific community.
-
- o Finnish University Network archive site
- o ---------------------------------------
-
- A large collection of neural network papers and software at
- ftp://ftp.funet.fi/pub/sci/neural/ Contains all the public domain
- software and papers that they have been able to find. All of these
- files have been transferred from FTP sites in U.S. and are mirrored
- about every 3 months at fastest. Contact: neural-adm@ftp.funet.fi
-
- o SEL-HPC Article Archive
- o -----------------------
-
- http://liinwww.ira.uka.de/bibliography/Misc/SEL-HPC.html
-
- o Machine Learning Papers
- o -----------------------
-
- http://gubbio.cs.berkeley.edu/mlpapers/
-
- o Plain-text Tables of Contents of NN journals
- o ++++++++++++++++++++++++++++++++++++++++++++
-
- Pattern Recognition Group, Department of Applied Physics,
- Faculty of Applied Sciences, Delft University of Technology,
- http://www.ph.tn.tudelft.nl/PRInfo/PRInfo/journals.html
-
- o The Collection of Computer Science Bibliographies:
- o ++++++++++++++++++++++++++++++++++++++++++++++++++
- Bibliographies on Neural Networks
- +++++++++++++++++++++++++++++++++
-
- http://liinwww.ira.uka.de/bibliography/Neural/index.html
-
- o BibTeX data bases of NN journals
- o ++++++++++++++++++++++++++++++++
-
- The Center for Computational Intelligence maintains BibTeX data bases of
- various NN journals, including IEEE Transactions on Neural Networks,
- Machine Learning, Neural Computation, and NIPS, at
- http://www.ci.tuwien.ac.at/docs/ci/bibtex_collection.html or
- ftp://ftp.ci.tuwien.ac.at/pub/texmf/bibtex/bib/.
-
- o NN events server
- o ++++++++++++++++
-
- There is a WWW page for Announcements of Conferences, Workshops and Other
- Events on Neural Networks at IDIAP in Switzerland. WWW-Server:
- http://www.idiap.ch/html/idiap-networks.html.
-
- o Academic programs list
- o ++++++++++++++++++++++
-
- Rutvik Desai <rutvik@c3serve.c3.lanl.gov> has a compilation of acedemic
- programs offering interdeciplinary studies in computational neuroscience,
- AI, cognitive psychology etc. at
- http://www.cs.indiana.edu/hyplan/rudesai/cogsci-prog.html
-
- Links to neurosci, psychology, linguistics lists are also provided.
-
- o Neurosciences Internet Resource Guide
- o +++++++++++++++++++++++++++++++++++++
-
- This document aims to be a guide to existing, free, Internet-accessible
- resources helpful to neuroscientists of all stripes. An ASCII text
- version (86K) is available in the Clearinghouse of Subject-Oriented
- Internet Resource Guides as follows:
-
- ftp://una.hh.lib.umich.edu/inetdirsstacks/neurosci:cormbonario,
- gopher://una.hh.lib.umich.edu/00/inetdirsstacks/neurosci:cormbonario,
- http://http2.sils.umich.edu/Public/nirg/nirg1.html.
-
- o Other WWW sites
- o +++++++++++++++
-
- In World-Wide-Web (WWW, for example via the xmosaic program) you can read
- neural network information for instance by opening one of the following
- uniform resource locators (URLs):
- http://www-xdiv.lanl.gov/XCM/neural/neural_announcements.html Los Alamos
- neural announcements and general information,
- http://www.ph.kcl.ac.uk/neuronet/ (NEuroNet, King's College, London),
- http://www.eeb.ele.tue.nl (Eindhoven, Netherlands),
- http://www.emsl.pnl.gov:2080/docs/cie/neural/ (Pacific Northwest National
- Laboratory, Richland, Washington, USA),
- http://www.cosy.sbg.ac.at/~rschwaig/rschwaig/projects.html (Salzburg,
- Austria),
- http://http2.sils.umich.edu/Public/nirg/nirg1.html (Michigan, USA),
- http://www.lpac.ac.uk/SEL-HPC/Articles/NeuralArchive.html (London),
- http://rtm.science.unitn.it/ Reactive Memory Search (Tabu Search) page
- (Trento, Italy),
- http://www.wi.leidenuniv.nl/art/ (ART WWW site, Leiden, Netherlands),
- http://nucleus.hut.fi/nnrc/ Helsinki University of Technology.
- http://www.pitt.edu/~mattf/NeuroRing.html links to neuroscience web pages
-
- http://www.arcade.uiowa.edu/hardin-www/md-neuro.htmlHardin Meta Directory
- web page for Neurology/Neurosciences.
- Many others are available too; WWW is changing all the time.
-
- ------------------------------------------------------------------------
-
- That's all folks (End of the Neural Network FAQ).
-
- Acknowledgements: Thanks to all the people who helped to get the stuff
- above into the posting. I cannot name them all, because
- I would make far too many errors then. :->
-
- No? Not good? You want individual credit?
- OK, OK. I'll try to name them all. But: no guarantee....
-
- THANKS FOR HELP TO:
- (in alphabetical order of email adresses, I hope)
-
- o Steve Ward <71561.2370@CompuServe.COM>
- o Allen Bonde <ab04@harvey.gte.com>
- o Accel Infotech Spore Pte Ltd <accel@solomon.technet.sg>
- o Ales Krajnc <akrajnc@fagg.uni-lj.si>
- o Alexander Linden <al@jargon.gmd.de>
- o Matthew David Aldous <aldous@mundil.cs.mu.OZ.AU>
- o S.Taimi Ames <ames@reed.edu>
- o Axel Mulder <amulder@move.kines.sfu.ca>
- o anderson@atc.boeing.com
- o Andy Gillanders <andy@grace.demon.co.uk>
- o Davide Anguita <anguita@ICSI.Berkeley.EDU>
- o Avraam Pouliakis <apou@leon.nrcps.ariadne-t.gr>
- o Kim L. Blackwell <avrama@helix.nih.gov>
- o Mohammad Bahrami <bahrami@cse.unsw.edu.au>
- o Paul Bakker <bakker@cs.uq.oz.au>
- o Stefan Bergdoll <bergdoll@zxd.basf-ag.de>
- o Jamshed Bharucha <bharucha@casbs.Stanford.EDU>
- o Carl M. Cook <biocomp@biocomp.seanet.com>
- o Yijun Cai <caiy@mercury.cs.uregina.ca>
- o L. Leon Campbell <campbell@brahms.udel.edu>
- o Cindy Hitchcock <cindyh@vnet.ibm.com>
- o Clare G. Gallagher <clare@mikuni2.mikuni.com>
- o Craig Watson <craig@magi.ncsl.nist.gov>
- o Yaron Danon <danony@goya.its.rpi.edu>
- o David Ewing <dave@ndx.com>
- o David DeMers <demers@cs.ucsd.edu>
- o Denni Rognvaldsson <denni@thep.lu.se>
- o Duane Highley <dhighley@ozarks.sgcl.lib.mo.us>
- o Dick.Keene@Central.Sun.COM
- o DJ Meyer <djm@partek.com>
- o Donald Tveter <don@dontveter.com>
- o Daniel Tauritz <dtauritz@wi.leidenuniv.nl>
- o Wlodzislaw Duch <duch@phys.uni.torun.pl>
- o E. Robert Tisdale <edwin@flamingo.cs.ucla.edu>
- o Athanasios Episcopos <episcopo@fire.camp.clarkson.edu>
- o Frank Schnorrenberg <fs0997@easttexas.tamu.edu>
- o Gary Lawrence Murphy <garym@maya.isis.org>
- o gaudiano@park.bu.edu
- o Lee Giles <giles@research.nj.nec.com>
- o Glen Clark <opto!glen@gatech.edu>
- o Phil Goodman <goodman@unr.edu>
- o guy@minster.york.ac.uk
- o Horace A. Vallas, Jr. <hav@neosoft.com>
- o Joerg Heitkoetter <heitkoet@lusty.informatik.uni-dortmund.de>
- o Ralf Hohenstein <hohenst@math.uni-muenster.de>
- o Ian Cresswell <icressw@leopold.win-uk.net>
- o Gamze Erten <ictech@mcimail.com>
- o Ed Rosenfeld <IER@aol.com>
- o Franco Insana <INSANA@asri.edu>
- o Janne Sinkkonen <janne@iki.fi>
- o Javier Blasco-Alberto <jblasco@ideafix.cps.unizar.es>
- o Jean-Denis Muller <jdmuller@vnet.ibm.com>
- o Jeff Harpster <uu0979!jeff@uu9.psi.com>
- o Jonathan Kamens <jik@MIT.Edu>
- o J.J. Merelo <jmerelo@geneura.ugr.es>
- o Dr. Jacek Zurada <jmzura02@starbase.spd.louisville.edu>
- o Jon Gunnar Solheim <jon@kongle.idt.unit.no>
- o Josef Nelissen <jonas@beor.informatik.rwth-aachen.de>
- o Joey Rogers <jrogers@buster.eng.ua.edu>
- o Subhash Kak <kak@gate.ee.lsu.edu>
- o Ken Karnofsky <karnofsky@mathworks.com>
- o Kjetil.Noervaag@idt.unit.no
- o Luke Koops <koops@gaul.csd.uwo.ca>
- o Kurt Hornik <Kurt.Hornik@tuwien.ac.at>
- o Thomas Lindblad <lindblad@kth.se>
- o Clark Lindsey <lindsey@particle.kth.se>
- o Lloyd Lubet <llubet@rt66.com>
- o William Mackeown <mackeown@compsci.bristol.ac.uk>
- o Maria Dolores Soriano Lopez <maria@vaire.imib.rwth-aachen.de>
- o Mark Plumbley <mark@dcs.kcl.ac.uk>
- o Peter Marvit <marvit@cattell.psych.upenn.edu>
- o masud@worldbank.org
- o Miguel A. Carreira-Perpinan<mcarreir@moises.ls.fi.upm.es>
- o Yoshiro Miyata <miyata@sccs.chukyo-u.ac.jp>
- o Madhav Moganti <mmogati@cs.umr.edu>
- o Jyrki Alakuijala <more@ee.oulu.fi>
- o Jean-Denis Muller <muller@bruyeres.cea.fr>
- o Michael Reiss <m.reiss@kcl.ac.uk>
- o mrs@kithrup.com
- o Maciek Sitnik <msitnik@plearn.edu.pl>
- o R. Steven Rainwater <ncc@ncc.jvnc.net>
- o Nigel Dodd <nd@neural.win-uk.net>
- o Barry Dunmall <neural@nts.sonnet.co.uk>
- o Paolo Ienne <Paolo.Ienne@di.epfl.ch>
- o Paul Keller <pe_keller@ccmail.pnl.gov>
- o Peter Hamer <P.G.Hamer@nortel.co.uk>
- o Pierre v.d. Laar <pierre@mbfys.kun.nl>
- o Michael Plonski <plonski@aero.org>
- o Lutz Prechelt <prechelt@ira.uka.de> [creator of FAQ]
- o Richard Andrew Miles Outerbridge <ramo@uvphys.phys.uvic.ca>
- o Rand Dixon <rdixon@passport.ca>
- o Robin L. Getz <rgetz@esd.nsc.com>
- o Richard Cornelius <richc@rsf.atd.ucar.edu>
- o Rob Cunningham <rkc@xn.ll.mit.edu>
- o Robert.Kocjancic@IJS.si
- o Randall C. O'Reilly <ro2m@crab.psy.cmu.edu>
- o Rutvik Desai <rudesai@cs.indiana.edu>
- o Robert W. Means <rwmeans@hnc.com>
- o Stefan Vogt <s_vogt@cis.umassd.edu>
- o Osamu Saito <saito@nttica.ntt.jp>
- o Scott Fahlman <sef+@cs.cmu.edu>
- o <seibert@ll.mit.edu>
- o Sheryl Cormicle <sherylc@umich.edu>
- o Ted Stockwell <ted@aps1.spa.umn.edu>
- o Stephanie Warrick <S.Warrick@cs.ucl.ac.uk>
- o Serge Waterschoot <swater@minf.vub.ac.be>
- o Thomas G. Dietterich <tgd@research.cs.orst.edu>
- o Thomas.Vogel@cl.cam.ac.uk
- o Ulrich Wendl <uli@unido.informatik.uni-dortmund.de>
- o M. Verleysen <verleysen@dice.ucl.ac.be>
- o VestaServ@aol.com
- o Sherif Hashem <vg197@neutrino.pnl.gov>
- o Matthew P Wiener <weemba@sagi.wistar.upenn.edu>
- o Wesley Elsberry <welsberr@centralneuralsystem.com>
- o Dr. Steve G. Romaniuk <ZLXX69A@prodigy.com>
-
- Special thanks to Gregory E. Heath <heath@ll.mit.edu> and Will Dwinnell
- <predictor@delphi.com> for years of stimulating and educational discussions
- on comp.ai.neurtal-nets.
-
- The FAQ was created in June/July 1991 by Lutz Prechelt; he also maintained
- the FAQ until November 1995. Warren Sarle maintains the FAQ since December
- 1995.
-
-
- Bye
-
- Warren & Lutz
-
- Previous part is part 6.
-
- Neural network FAQ / Warren S. Sarle, saswss@unx.sas.com
-
- --
-
- Warren S. Sarle SAS Institute Inc. The opinions expressed here
- saswss@unx.sas.com SAS Campus Drive are mine and not necessarily
- (919) 677-8000 Cary, NC 27513, USA those of SAS Institute.
-