Outline

1. Files Needed or Produced by Software
2. Training and Testing Data 
 a. Standard Format
 b. Data Files Included With This Package
3. File and Neural Net Limitations


1. Files Needed or Produced by Software

a. MLP and functional link neural networks typically have three types 
   of files associated with them. These three types are: 
 (1) The network structure file. For the MLP, this file specifies the 
     number of network layers, the number of artificial neurons 
     (called units) in each layer, and the number of the first layer 
     which the third and fourth (if there is one) layers connect to.
     For the functional link net, this file contains the network degree P
     (usually an integer between 1 and 5), the number of network inputs N
     and the number of outputs, and the dimension of the multinomial vector, 
     which is L = (N+P)!/(N!P!).

 (2) The weight file, which gives the gains or coefficients along 
     paths connecting the various units.

 (3) The training or testing data file, which gives example inputs 
     and outputs for network learning, or for testing after learning.

b. The network structure files have the extension "top". You can create 
   your own network structure files within the backpropagation, fast 
   training and functional link programs, if you want. Consider the MLP 
   network structure file, GLS.top shown below. 

           4
           4          20          15           1
           1           1           1

  It has 4 layers. The first layer has 4 inputs, which means that 
  each training or testing pattern has 4 numbers. It has 20 units in 
  the first hidden layer, where "hidden" means that it is not an input 
  or output layer. It has 15 units in the second hidden layer. 
  The output layer has 1 unit. The last line of "1s" means that 
  layers 2, 3, and 4 connect up with layer 1, layers 1 and 2, and 
  layers 1, 2, and 3 respectively. This network is "fully connected",
  meaning that each layer connects with all previous layers. Fully 
  connected networks are more powerful than and train faster than 
  non fully connected networks. The fully connected networks are
  almost always smaller than non fully connected networks which 
  perform the same operation.

2. Training and Testing Data
 a. Standard Format
   All data files are in standard form. Standard form means that
   the file is formatted, and that each pattern or vector has inputs
   on the left and desired outputs on the right. You can type 
   out the files to examine them, and you can use these 
   files with other neural net software. For example, consider the 
   training data file, Max, part of which is shown below.

     .5844768      .5359043      .6196933
     .6196933

     .1291312      .4173794      .3405759
     .4173794

     .0472856      .5994965      .5638752
     .5994965

   Each training pattern consists of three random numbers. The fourth 
   number, which is the desired network output, is the maximum of the 
   three inputs. 

 b. Data Files Included With This Package

    The MAX data file, which corresponds to calculating the maximum
       of 3 random numbers, has 300 patterns, each of which has 3 inputs
       and 1 desired output.
    The GLS data file has 300 training patterns, with 4 inputs and 1
       desired output. Each pattern contains samples from times T,
       T-6, T-12 and T-18, of the chaotic time series created 
       by the Mackey-Glass delay-difference equation, (a = 0.2,b = 0.1,
       tau = 17), as Inputs. The sample at time T+6 is the desired output.
  
       (Ref. Lapedes, A. & Farber, R.1987 Nonlinear Signal processing using
       Neural Networks : Prediction & System modelling,  Tech. Rep. 
       LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, NM.)
    The twod.tra data file has 1,768 patterns with 8 inputs and 7 desired 
       outputs. The data comes from a remote sensing problem.

3. File and Neural Net Limitations
 There is no limitation on data file size.
 MLP neural nets are limited to 40 or fewer units in each layer,
   including the input layer, one or two hidden layers, and the output
   layer.
 Functional link networks are limited to 40 inputs, 20 outputs, and
   5th degree.
 Conventional clustering and self-organizing map clustering are limited 
   to 32 elements per vector and 2,048 clusters. There is no limit on 
   the number of input patterns.
