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This is to be expected, as most logic programming systems are experimental. ---------------------------------------------------------------- ;;; *EOF* 9Vw1r9Fs*FVFV06&X&& FVFVFVPPFPC uP6d6bVh4 pP~QPVh4 3,602&9?~Dv3ɉNރ,.&Gt &u&g[GĞ0&9?ЋvFP[Ğ0&/P86&6&6VW V2 FPFPR ^_]ːUTWVN6&6 &6FP PFPh PFP V t)FPVQpQ~RQx WV ^_]ːU"WVFP( FP0 V<&m&EFT6&F&M FF &EF~,6&F 86&&؉V$6&FN9Nu~t F& Minds and Machines 2(4) November 1992 was devoted to music and cognition, and there were some relevant book reviews in 3(1) February 1993. ---------------------------------------------------------------- ;;; *EOF* uZFw&*FtJF&F t F&8Eu4Fw&F t F&8EuFw&FF;F}F[A9N~V~t}^_]ːear all searchUWVP V t5P6  u$FPVVP VPƆVPVPh FPVPXX  t$VPP(P P ~WP V u P(P P P-PVP26 FV u$VP0P(P P vvWPvV ;t2VPLP(P vv P vv  t$VPwP(P P F  3RT&7&GLX"3NP&&G&MVv~~؎ZX&&+NjV&&PIuvĞN&F&7&GĞR&&&GÌV&$3& &> & & ߎ&&3&OF&&&ʋH(3JL&&G*3&&F&ʋFPQ" @FĞJ&&?~G~VvN~&&+NjV&&PAĞJ&&9ڋ~v*3&F&7&GĞJ&&@&GF,3&> & M.3& & ^_]ː&u0 U.WV4Z9&3\9&6^9&"`9& b9&r d9&bf9&dh9&fj9&h+ɉNNvvZ RP"Pr QP V^V۹^VNVl9&>nu-n9&X&B&@ "P| 3br QP t~ PvvP t!2PP] p9&tN릐2PP] p9&u, QP2PP] p9&u0 ~+PP( FVP+PP( ȋ+FV~9&&SQvvvVr FV~9&&d3SQRP FVdRPvv FVvvRP<RPSQRPvv⚺ RPFމVT SQvvމFډVܚT <3SQvvF։Vؚquest@cs.cmu.edu. 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Several implementations have a mouse sensitivity feature and others have implementation-specific features. A copy has been made available from the Lisp Utilities Repository. 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FPQ\%QJ] PvV" PvVJ] PvvvJ] F~~;~v^^؋NJ*3QNJ*&3ZF;v|։~v] v_%PvV PvV" PvVJ] FF=vP FVvv`  t 2PP]  :&uvv`  t|] u t 3P( -*ui%PvV vV2 ui6`#6^#vVF  uN6p#6n#vvF  t~9&X&&e%PFPW ƋFv2PP] 9&X&&V\2PP]  :&u9&X&&ˎ9&X&&k%PFPV NjFvW 3^_]U WVFdžPƆ{dž8o%:dždž@~%BdžD%FdžH%J+NL9& & vx~prt89 +&G&n9&9ptH9&9vt=PP9&6&6`%P9&6&6ZT %P| T :&P FVpP FV+FF{{<vv~uZ6`#6^#%P~V P~V" P~VJ] ship function for the fuzzy value. In the MAXIMUM method, one of the variable values at which the fuzzy subset has its maximum truth value is chosen as the crisp value for the output variable. Extended Example: Assume that the variables x, y, and z all take on values in the interval [0,10], and that the following membership functions and rules are defined: low(t) = 1 - ( t / 10 ) high(t) = t / 10 rule 1: if x is low and y is low then z is high rule 2: if x is low and y is high then z is low rule 3: if x is high and y is low then z is low rule 4: if x is high and y is high then z is high Notice that instead of assigning a single value to the output variable z, each rule assigns an entire fuzzy subset (low or high). Notes: 1. In this example, low(t)+high(t)=1.0 for all t. This is not required, but it is fairly common. 2. The value of t at which low(t) is maximum is the same as the value of t at which high(t) is minimum, and vice-versa. This is also not required, but fairly common. 3. The same membership functions are used for all variables. This isn't required, and is also *not* common. In the fuzzification subprocess, the membership functions defined on the input variables are applied to their actual values, to determine the degree of truth for each rule premise. The degree of truth for a rule's premise is sometimes referred to as its ALPHA. If a rule's premise has a nonzero degree of truth (if the rule applies at all...) then the rule is said to FIRE. For example, x y low(x) high(x) low(y) high(y) alpha1 alpha2 alpha3 alpha4 ------------------------------------------------------------------------------ 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 3.2 1.0 0.0 0.68 0.32 0.68 0.32 0.0 0.0 0.0 6.1 1.0 0.0 0.39 0.61 0.39 0.61 0.0 0.0 0.0 10.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 3.2 0.0 0.68 0.32 1.0 0.0 0.68 0.0 0.32 0.0 6.1 0.0 0.39 0.61 1.0 0.0 0.39 0.0 0.61 0.0 10.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 3.2 3.1 0.68 0.32 0.69 0.31 0.68 0.31 0.32 0.31 3.2 3.3 0.68 0.32 0.67 0.33 0.67 0.33 0.32 0.32 10.0 10.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 In the inference subprocess, the truth value for the premise of each rule is computed, and applied to the conclusion part of each rule. This results in one fuzzy subset to be assigned to each output variable for each rule. MIN and PRODUCT are two INFERENCE METHODS or INFERENCE RULES. In MIN inferencing, the output membership function is clipped off at a height corresponding to the rule premise's computed degree of truth. This corresponds to the traditional interpretation of the fuzzy logic AND operation. In PRODUCT inferencing, the output membership function is scaled by the rule premise's computed degree of truth. For example, let's look at rule 1 for x = 0.0 and y = 3.2. As shown in the table above, the premise degree of truth works out to 0.68. For this rule, MIN inferencing will assign z the fuzzy subset defined by the membership function: rule1(z) = { z / 10, if z <= 6.8 0.68, if z >= 6.8 } For the same conditions, PRODUCT inferencing will assign z the fuzzy subset defined by the membership function: rule1(z) = 0.68 * high(z) = 0.068 * z Note: The terminology used here is slightly nonstandard. In most texts, the term "inference method" is used to mean the combination of the things referred to separately here as "inference" and "composition." Thus you'll see such terms as "MAX-MIN inference" and "SUM-PRODUCT inference" in the literature. They are the combination of MAX composition and MIN inference, or SUM composition and PRODUCT inference, respectively. You'll also see the reverse terms "MIN-MAX" and "PRODUCT-SUM" -- these mean the same things as the reverse order. It seems clearer to describe the two processes separately. In the composition subprocess, all of the fuzzy subsets assigned to each output variable are combined together to form a single fuzzy subset for each output variable. MAX composition and SUM composition are two COMPOSITION RULES. In MAX composition, the combined output fuzzy subset is constructed by taking the pointwise maximum over all of the fuzzy subsets assigned to the output variable by the inference rule. In SUM composition, the combined output fuzzy subset is constructed by taking the pointwise sum over all of the fuzzy subsets assigned to the output variable by the inference rule. Note that this can result in truth values greater than one! For this reason, SUM composition is only used when it will be followed by a defuzzification method, such as the CENTROID method, that doesn't have a problem with this odd case. Otherwise SUM composition can be combined with normalization and is therefore a general purpose method again. For example, assume x = 0.0 and y = 3.2. MIN inferencing would assign the following four fuzzy subsets to z: rule1(z) = { z / 10, if z <= 6.8 0.68, if z >= 6.8 } rule2(z) = { 0.32, if z <= 6.8 1 - z / 10, if z >= 6.8 } rule3(z) = 0.0 rule4(z) = 0.0 MAX composition would result in the fuzzy subset: fuzzy(z) = { 0.32, if z <= 3.2 z / 10, if 3.2 <= z <= 6.8 0.68, if z >= 6.8 } PRODUCT inferencing would assign the following four fuzzy subsets to z: rule1(z) = 0.068 * z rule2(z) = 0.32 - 0.032 * z rule3(z) = 0.0 rule4(z) = 0.0 SUM composition would result in the fuzzy subset: fuzzy(z) = 0.32 + 0.036 * z Sometimes it is useful to just examine the fuzzy subsets that are the result of the composition process, but more often, this FUZZY VALUE needs to be converted to a single number -- a CRISP VALUE. This is what the defuzzification subprocess does. There are more defuzzification methods than you can shake a stick at. A couple of years ago, Mizumoto did a short paper that compared about ten defuzzification methods. Two of the more common techniques are the CENTROID and MAXIMUM methods. In the CENTROID method, the crisp value of the output variable is computed by finding the variable value of the center of gravity of the membership function for the fuzzy value. In the MAXIMUM method, one of the variable values at which the fuzzy subset has its maximum truth value is chosen as the crisp value for the output variable. There are several variations of the MAXIMUM method that differ only in what they do when there is more than one variable value at which this maximum truth value occurs. One of these, the AVERAGE-OF-MAXIMA method, returns the average of the variable values at which the maximum truth value occurs. For example, go back to our previous examples. Using MAX-MIN inferencing and AVERAGE-OF-MAXIMA defuzzification results in a crisp value of 8.4 for z. Using PRODUCT-SUM inferencing and CENTROID defuzzification results in a crisp value of 5.6 for z, as follows. Earlier on in the FAQ, we state that all variables (including z) take on values in the range [0, 10]. To compute the centroid of the function f(x), you divide the moment of the function by the area of the function. To compute the moment of f(x), you compute the integral of x*f(x) dx, and to compute the area of f(x), you compute the integral of f(x) dx. In this case, we would compute the area as integral from 0 to 10 of (0.32+0.036*z) dz, which is (0.32 * 10 + 0.018*100) = (3.2 + 1.8) = 5.0 and the moment as the integral from 0 to 10 of (0.32*z+0.036*z*z) dz, which is (0.16 * 10 * 10 + 0.012 * 10 * 10 * 10) = (16 + 12) = 28 Finally, the centroid is 28/5 or 5.6. Note: Sometimes the composition and defuzzification processes are combined, taking advantage of mathematical relationships that simplify the process of computing the final output variable values. The Mizumoto referece is probably "Improvement Methods of Fuzzy Controls", in Proceedings of the 3rd IFSA Congress, pages 60-62, 1989. ================================================================ Subject: [5] Where are fuzzy expert systems used? Date: 15-APR-93 To date, fuzzy expert systems are the most common use of fuzzy logic. They are used in several wide-ranging fields, including: o Linear and Nonlinear Control o Pattern Recognition o Financial Systems o Operation Research o Data Analysis ================================================================ Subject: [6] What is fuzzy control? Date: 15-APR-93 [Anybody want to write an answer?] References: Yager, R.R., and Zadeh, L. A., "An Introduction to Fuzzy Logic Applications in Intelligent Systems"ns", in Fuzzy Sets and Applications to Cognitive and Decision Making Processes, edited by L. A. Zadeh et. al., Academic Press, New York, 1975, pages 1-39. ================================================================ Subject: [3] Where is fuzzy logic used? Date: 15-APR-93 Fuzzy logic is used directly in very few applications. The Sony PalmTop apparently uses a fuzzy logic decision tree algorithm to perform handwritten (well, computer lightpen) Kanji character recognition. Most applications of fuzzy logic use it as the underlying logic system for fuzzy expert systems (see [4]). ================================================================ Subject: [4] What is a fuzzy expert system? Date: 21-APR-93 A fuzzy expert system is an expert system that uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to reason about data. The rules in a fuzzy expert system are usually of a form similar to the following: if x is low and y is high then z = medium where x and y are input variables (names for know data values), z is an output variable (a name for a data value to be computed), low is a membership function (fuzzy subset) defined on x, high is a membership function defined on y, and medium is a membership function defined on z. The antecedent (the rule's premise) describes to what degree the rule applies, while the conclusion (the rule's consequent) assigns a membership function to each of one or more output variables. Most tools for working with fuzzy expert systems allow more than one conclusion per rule. The set of rules in a fuzzy expert system is known as the rulebase or knowledge base. The general inference process proceeds in three (or four) steps. 1. Under FUZZIFICATION, the membership functions defined on the input variables are applied to their actual values, to determine the degree of truth for each rule premise. 2. Under INFERENCE, the truth value for the premise of each rule is computed, and applied to the conclusion part of each rule. This results in one fuzzy subset to be assigned to each output variable for each rule. Usually only MIN or PRODUCT are used as inference rules. In MIN inferencing, the output membership function is clipped off at a height corresponding to the rule premise's computed degree of truth (fuzzy logic AND). In PRODUCT inferencing, the output membership function is scaled by the rule premise's computed degree of truth. 3. Under COMPOSITION, all of the fuzzy subsets assigned to each output variable are combined together to form a single fuzzy subset for each output variable. Again, usually MAX or SUM are used. In MAX composition, the combined output fuzzy subset is constructed by taking the pointwise maximum over all of the fuzzy subsets assigned tovariable by the inference rule (fuzzy logic OR). In SUM composition, the combined output fuzzy subset is constructed by taking the pointwise sum over all of the fuzzy subsets assigned to the output variable by the inference rule. 4. Finally is the (optional) DEFUZZIFICATION, which is used when it is useful to convert the fuzzy output set to a crisp number. There are more defuzzification methods than you can shake a stick at (at least 30). Two of the more common techniques are the CENTROID and MAXIMUM methods. In the CENTROID method, the crisp value of the output variable is computed by finding the variable value of the center of gravity of the member probability curves are used as the basis to construct a membership function. There are a variety of possible conversion methods, each with its own mathematical and methodological strengths and weaknesses. However, it should always be remembered that membership functions are NOT (necessarily) probabilities. See [10] for more information. 4. Physical measurement Many applications of fuzzy logic use physical measurement, but almost none measure the membership grade directly. Instead, a membership function is provided by another method, and then the individual membership grades of data are calculated from it (see FUZZIFICATION in [4]). 5. Learning and adaptation For more information, see: Roberts, D.W., "Analysis of Forest Succession with Fuzzy Graph Theory", Ecological Modeling, 45:261-274, 1989. Turksen, I.B., "Measurement of Fuzziness: Interpretiation of the Axioms of Measure", in Proceeding of the Conference on Fuzzy Information and Knowledge Representation for Decision Analysis, pages 97-102, IFAC, Oxford, 1984. ================================================================ Subject: [10] What is the relationship between fuzzy truth values and probabilities? Date: 15-APR-93 Fuzzy values are commonly misunderstood to be probabilities, or fuzzy logic is interpreted as some new way of handling probabilities. But this is not the case. A minimum requirement of probabilities is ADDITIVITY, that is that they must add together to one, or the integral of their density curves must be one. But this is not the case in general with membership grades. And while membership grades can be determined with probability densities in mind (see [11]), there are other methods as well which have nothing to do with frequencies or probabilities. Because of this, fuzzy researchers have gone to great pains to distance themselves from probability. But in so doing, many of them have lost track of another point, which is that the converse DOES hold: all probability distributions are fuzzy sets! As fuzzy sets and logic generalize Boolean sets and logic, they also generalize probability. In fact, from a mathematical perspective, fuzzy sets and probability exist as parts of a greater Generalized Information Theory which also includes random sets, Demster-Shafer evidence theory, probability intervals, possibility theory, fuzzy measures, and so on. Furthermore, one can also talk about random fuzzy events and fuzzy random events. This whole issue is beyond the scope of this FAQ, so please refer to the following articles, or the textbook by Klir and Folger (see [16]). Delgado, M., and Moral, S., "On the Concept of Possibility-Probability Consistency", Fuzzy Sets and Systems 21:311-318, 1987. Dempster, A.P., "Upper and Lower Probabilities Induced by a Multivalued Mapping", Annals of Math. Stat. 38:325-339, 1967. Henkind, Steven J., and Harrison, Malcolm C., "Analysis of Four Uncertainty Calculi", IEEE Trans. Man Sys. Cyb. 18(5)700-714, 1988. Kamp`e de, F'eriet J., "Interpretation of Membership Functions of Fuzzy Sets in Terms of Plausibility and Belief", in Fuzzy Information and Decision Process, M.M. Gupta and E. Sanchez (editors), pages 93-98, North-Holland, Amsterdam, 1982. Klir, George, "Is There More to Uncertainty than Some Probability Theorists Would Have Us Believe?", Int. J. Gen. Sys. 15(4):347-378, 1989. Klir, George, "Generalized Information Theory", Fuzzy Sets and Systems 40:127-142, 1991. Klir, George, "Probabilistic vs. Possibilistic Conceptualization of Uncertainty", in Analysis and Management of Uncertainty, B.M. Ayyub et. al. (editors), pages 13-25, Elsevier, 1992. Klir, George, and Parviz, Behvad, "Probability-Possibility Transformations: A Comparison", Int. J. Gen. Sys. 21(1):291-310, 1992. Kosko, B., "Fuzziness vs. Probability", Int. J. Gen. Sys. 17(2-3):211-240, 1990. Puri, M.L., and Ralescu, D.A., "Fuzzy Random Variables", J. Math. Analysis and Applications, 114:409-422, 1986. Shafer, Glen, "A Mathematical Theory of Evidence", Princeton University, Princeton, 1976. ================================================================ Subject: [11] Are there fuzzy state machines? Date: 15-APR-93 Yes. FSMs are obtained by assigning membership grades as weights to the states of a machine, weights on transitions between states, and then a composition rule such as MAX/MIN or PLUS/TIMES (see [4]) to calculate new grades of future states. Refer to the following article, or to Section III of the Dubois and Prade's 1980 textbook (see [16]). Gaines, Brian R., and Kohout, Ladislav J., "Logic of Automata", Int. J. Gen. Sys. 2(4):191-208, 1976. ================================================================ Subject: [12] What is possibility theory? Date: 15-APR-93 Possibility theory is a new form of information theory which is related to but independent of both fuzzy sets and probability theory. Technically, a possibility distribution is a normal fuzzy set (at least one membership grade equals 1). For example, all fuzzy numbers are possibility distributions. However, possibility theory can also be derived without reference to fuzzy sets. The rules of possibility theory are similar to probability theory, but use either MAX/MIN or MAX/TIMES calculus, rather than the PLUS/TIMES calculus of probability theory. Also, possibilistic NONSPECIFICITY is available as a measure of information similar to the stochastic ENTROPY. Possibility theory has a methodological advantage over probability theory as a representation of nondeterminism in systems, because the PLUS/TIMES calculus does not validly generalize nondeterministic processes, while MAX/MIN and MAX/TIMES do. For further information, see: Dubois, Didier, and Prade, Henri, "Possibility Theory", Plenum Press, New York, 1988. Joslyn, Cliff, "Possibilistic Measurement and Set Statistics", in Proceedings of the 1992 NAFIPS Conference 2:458-467, NASA, 1992. Joslyn, Cliff, "Possibilistic Semantics and Measurement Methods in Complex Systems", in Proceedings of the 2nd International Symposium on Uncertainty Modeling and Analysis, Bilal Ayyub (editor), IEEE Computer Society 1993. Wang, Zhenyuan, and Klir, George J., "Fuzzy Measure Theory", Plenum Press, New York, 1991. Zadeh, Lotfi, "Fuzzy Sets as the Basis for a Theory of Possibility", Fuzzy Sets and Systems 1:3-28, 1978. ================================================================ Subject: [13] How can I get a copy of the proceedings for ? Date: 15-APR-93 This is rough sometimes. The first thing to do, of course, is to contact the organization that ran the conference or workshop you are interested in. If they can't help you, the best idea mentioned so far is to contact the Institute for Scientific Information, Inc. (ISI), and check with their Index to Scientific and Technical Proceedings (ISTP volumes). Institute for Scientific Information, Inc. 3501 Market Street Philadelphia, PA 19104, USA Phone: +1.215.386.0100 Fax: +1.215.386.6362 Cable: SCINFO Telex: 84-5305 ================================================================ Subject: [14] Fuzzy BBS Systems, Mail-servers and FTP Repositories Date: 24-AUG-93 Aptronix FuzzyNET BBS and Email Server: 408-428-1883, 1200-9600 N/8/1 This BBS contains a range of fuzzy-related material, including: o Application notes. o Product brochures. o Technical information. o Archived articles from the USENET newsgroup comp.ai.fuzzy. o Text versions of "The Huntington Technical Brief" by Dr. Brubaker. [The technical brief is no longer being updated, as Dr. Brubaker now charges for subscriptions. See [17] for details.] The Aptronix FuzzyNET Email Server allows anyone with access to Internet email access to all of the files on the FuzzyNET BBS. To receive instructions on how to access the server, send the following message to fuzzynet@aptronix.com: begin help end If you don't receive a response within a day or two, or need help, contact Scott Irwin for assistance. Electronic Design News (EDN) BBS: 617-558-4241, 1200-9600 N/8/1 Motorola FREEBBS: 512-891-3733, 1200-9600 E/7/1 Ostfold Regional College Fuzzy Logic Anonymous FTP Repository: ftp.dhhalden.no:pub/Fuzzy/ is a recently-started ftp site for fuzzy-related material, operated by Ostfold Regional College in Norway. Currently has files from the Togai InfraLogic Fuzzy Logic Email Server, Tim Butler's Fuzzy Logic Anonymous FTP Repository, some demo programs and source code, and lists of upcoming conferences, articles, and literature about fuzzy logic. Material to be included in the archive (e.g., papers and code) may be placed in the incoming/ directory. Send email to Randi Weberg . Tim Butler's Fuzzy Logic Anonymous FTP Repository & Email Server: ntia.its.bldrdoc.gov:pub/fuzzy contains information concerning fuzzy logic, including bibliographies (bib/), product descriptions and demo versions (com/), machine readable published papers (lit/), miscellaneous information, documents and reports (txt/), and programs code and compilers (prog/). You may download new items into the new/ subdirectory, or send them by email to fuzzy@its.bldrdoc.gov. If you deposit anything in new/, please inform fuzzy@its.bldrdoc.gov. The repository is maintained by Timothy Butler, tim@its.bldrdoc.gov. The Fuzzy Logic Repository is also accessible through a mail server, rnalib@its.bldrdoc.gov. For help on using the server, send mail to the server with the following line in the body of the message: @@ help Togai InfraLogic Fuzzy Logic Email Server: The Togai InfraLogic Fuzzy Logic Email Server allows anyone with access to Internet email access to: o PostScript copies of TIL's company newsletter, The Fuzzy Source. o ASCII files for selected newsletter articles. o Archived articles from the USENET newsgroup comp.ai.fuzzy. o Fuzzy logic demonstration programs. o Demonstration versions of TIL products. o Conference announcements. o User-contributed files. To receive instructions on how to access the server, send the following message, with no subject, to fuzzy-server@til.com. help If you don't receive a response within a day or two, contact either erik@til.com or tanaka@til.com for assistance. Most of the contents of TIL's email server are mirrored by Tim Butler's Fuzzy Logic Anonymous FTP Repository and the Ostfold Regional College Fuzzy Logic Anonymous FTP Repository in Norway. The Turning Point BBS: 512-219-7828/7848, DS/HST 1200-19,200 N/8/1 Fuzzy logic and neural network related files. Miscellaneous Fuzzy Logic Files: The "General Purpose Fuzzy Reasoning Library" is available by anonymous FTP from utsun.s.u-tokyo.ac.jp:fj/fj.sources/v25/2577.Z [133.11.11.11]. This yields the "General-Purpose Fuzzy Inference Library Ver. 3.0 (1/1)". The program is in C, with English comments, but the documentation is in Japanese. Some English documentation has been written by John Nagle, . CNCL is a C++ class library provides classes for simulation, fuzzy logic, DEC's EZD, and UNIX system calls. It is available from ftp.dfv.rwth-aachen.de:pub/CNCL [137.226.4.111]. Contact Martin Junius for more information. A demo version of Aptronix's FIDE 2.0 is available by anonymous ftp from ftp.cs.cmu.edu:user/ai/areas/fuzzy/code/fide/. FIDE is a PC-based fuzzy logic design tool. It provides tools for the development, debugging, and simulation of fuzzy applications. For more information, contact info@aptronix.com. ================================================================ Subject: [15] Mailing Lists Date: 15-APR-93 The Fuzzy-Mail and NAFIPS-L mailing lists are now bidirectionally gatewayed to the comp.ai.fuzzy newsgroup. NAFIPS Fuzzy Logic Mailing List: This is a mailing list for the discussion of fuzzy logic, NAFIPS and related topics, located at the Georgia State University. The last time that this FAQ was updated, there were about 150 subscribers, located primarily in North America, as one might expect. Postings to the mailing list are automatically archived. The mailing list server itself is like most of those in use on the Internet. If you're already familiar with Internet mailing lists, the only thing you'll need to know is that the name of the server is listserv@gsuvm1.gsu.edu -or- listserv@gsuvm1.bitnet and the name of the mailing list itself is nafips-l@gsuvm1.gsu.edu -or- nafips-l@gsuvm1.bitnet Use the "gsuvm1.gsu.edu" addresses if you're on the Internet, and the "gsuvm1.bitnet" addresses if you're on BITNET. If you're on some other network, try to figure out which is "closer" to you, and use that one. If you're not familiar with this type of mailing list server, the easiest way to get started is to send the following message to listserv@gsuvm1.gsu.edu: help You will receive a brief set of instructions by email within a short time. Once you have subscribed, you will begin receiving a copy of each message that is sent by anyone to nafips-l@gsuvm1.gsu.edu, and any message that you send to that address will be sent to all of the other subscribers. Technical University of Vienna Fuzzy Logic Mailing List: This is a mailing list for the discussion of fuzzy logic and related topics, located at the Technical University of Vienna in Austria. The last time this FAQ was updated, there were about 420 subscribers. The list is slightly moderated (only irrelevant mails are rejected) and is two-way gatewayed to the aforementioned NAFIPS-L list and to the comp.ai.fuzzy internet newsgroup. Messages should therefore be sent only to one of the three media, although some mechanism for mail-loop avoidance and duplicate-message avoidance is activated. In addition to the mailing list itself, the list server gives access to some files, including archives and the "Who is Who in Fuzzy Logic" database that is currently under construction by Robert Fuller . Like many mailing lists, this one uses Anastasios Kotsikonas's LISTSERVER system. If you've used this kind of server before, the only thing you'll need to know is that the name of the server is listserver@vexpert.dbai.tuwien.ac.at and the name of the mailing list is fuzzy-mail@vexpert.dbai.tuwien.ac.at If you're not familiar with this type of mailing list server, the easiest way to get started is to send the following message to listserver@vexpert.dbai.tuwien.ac.at: get fuzzy-mail info You will receive a brief set of instructions by email within a short time. Once you have subscribed, you will begin receiving a copy of each message that is sent by anyone to fuzzy-mail@vexpert.dbai.tuwien.ac.at, and any message that you send to that address will be sent to all of the other subscribers. ================================================================ Subject: [16] Bibliography Date: 7-JUN-93 A list of books compiled by Josef Benedikt for the FLAI '93 (Fuzzy Logic in Artificial Intelligence) conference's book exhibition is available by anonymous ftp from ftp.cs.cmu.edu in the directory /afs/cs.cmu.edu/project/ai-repository/ai/pubs/bibs/ as the file fuzzy-bib.text. Non-Mathematical Works: Kosko, Bart, "Fuzzy Thinking: The New Science of Fuzzy Logic", Warner, 1993 McNeill, Daniel, and Freiberger, Paul, "Fuzzy Logic", Simon and Schuster, 1992. Negoita, C.V., "Fuzzy Systems", Abacus Press, Tunbridge-Wells, 1981. Smithson, Michael, "Ignorance and Uncertainty: Emerging Paradigms", Springer-Verlag, New York, 1988. Brubaker, D.I., "Fuzzy-logic Basics: Intuitive Rules Replace Complex Math," EDN, June 18, 1992. Schwartz, D.G. and Klir, G.J., "Fuzzy Logic Flowers in Japan," IEEE Spectrum, July 1992. Textbooks: Dubois, Didier, and Prade, H., "Fuzzy Sets and Systems: Theory and Applications", Academic Press, New York, 1980. Dubois, Didier, and Prade, Henri, "Possibility Theory", Plenum Press, New York, 1988. Goodman, I.R., and Nguyen, H.T., "Uncertainty Models for Knowledge-Based Systems", North-Holland, Amsterdam, 1986. Kandel, Abraham, "Fuzzy Mathematical Techniques with Applications", Addison-Wesley, 1986. Kandel, Abraham, and Lee, A., "Fuzzy Switching and Automata", Crane Russak, New York, 1979. Klir, George, and Folger, Tina, "Fuzzy Sets, Uncertainty, and Information", Prentice Hall, Englewood Cliffs, NJ, 1987. Kosko, B., "Neural Networks and Fuzzy Systems", Prentice Hall, Englewood Cliffs, NJ, 1992. Toshiro Terano, Kiyoji Asai, and Michio Sugeno, "Fuzzy Systems Theory and its Applications", Academic Press, 1992, 268 pages. ISBN 0-12-685245-6. Translation of "Fajii shisutemu nyumon" (Japanese, 1987). Wang, Paul P., "Theory of Fuzzy Sets and Their Applications", Shanghai Science and Technology, Shanghai, 1982. Wang, Zhenyuan, and Klir, George J., "Fuzzy Measure Theory", Plenum Press, New York, 1991. Yager, R.R., (editor), "Fuzzy Sets and Applications", John Wiley and Sons, New York, 1987. Zimmerman, Hans J., "Fuzzy Set Theory", Kluwer, Boston, 2nd edition, 1991. Anthologies: Didier Dubois, Henri Prade, and Ronald R. Yager, editors, "Readings in Fuzzy Systems", Morgan Kaufmann Publishers, 1992. "A Quarter Century of Fuzzy Systems", Special Issue of the International Journal of General Systems, 17(2-3), June 1990. ================================================================ Subject: [17] Journals and Technical Newsletters Date: 24-AUG-93 INTERNATIONAL JOURNAL OF APPROXIMATE REASONING (IJAR) Official publication of the North American Fuzzy Information Processing Society (NAFIPS). Published 8 times annually. ISSN 0888-613X. Subscriptions: Institutions $282, NAFIPS members $72 (plus $5 NAFIPS dues) $36 mailing surcharge if outside North America. For subscription information, write to David Reis, Elsevier Science Publishing Company, Inc., 655 Avenue of the Americas, New York, New York 10010, call 212-633-3827, fax 212-633-3913, or send email to 74740.2600@compuserve.com. Editor: Piero Bonissone Editor, Int'l J of Approx Reasoning (IJAR) GE Corp R&D Bldg K1 Rm 5C32A PO Box 8 Schenectady, NY 12301 USA Email: bonissone@crd.ge.com Voice: 518-387-5155 Fax: 518-387-6845 Email: Bonissone@crd.ge.com INTERNATIONAL JOURNAL OF FUZZY SETS AND SYSTEMS (IJFSS) The official publication of the International Fuzzy Systems Association. Subscriptions: Subscription is free to members of IFSA. ISSN: 0165-0114 IEEE TRANSACTIONS ON FUZZY SYSTEMS ISSN 1063-6706 Editor in Chief: James Bezdek THE HUNTINGTON TECHNICAL BRIEF Technical newsletter about fuzzy logic edited by Dr. Brubaker. It is mailed monthly, is a single sheet, front and back, and rotates among tutorials, descriptions of actual fuzzy applications, and discussions (reviews, sort of) of existing fuzzy tools and products. Subscriptions are $12 for an individual (paid by personal check), $24 otherwise. INTERNATIONAL JOURNAL OF UNCERTAINTY, FUZZINESS AND KNOWLEDGE-BASED SYSTEMS (IJUFKS) Published 4 times annually. ISSN 0218-4885. Intended as a forum for research on methods for managing imprecise, vague, uncertain and incomplete knowledge. Subscriptions: Individuals $90, Institutions $180. (add $25 for airmail) World Scientific Publishing Co Pte Ltd, Farer Road, PO Box 128, SINGAPORE 9128, e-mail phua@ictp.trieste.it, phone 65-382-5663, fax 65-382-5919. Submissions: B Bouchon-Meunier, editor in chief, Laforia-IBP, Universite Paris VI, Boite 169, 4 Place Jussieu, 75252 Paris Cedex 05, FRANCE, phone 33-1-44-27-70-03, fax 33-1-44-27-70-00, e-mail bouchon@laforia.ibp.fr. ================================================================ Subject: [18] Professional Organizations Date: 15-APR-93 INSTITUTION FOR FUZZY SYSTEMS AND INTELLIGENT CONTROL, INC. Sponsors, organizes, and publishes the proceedings of the International Fuzzy Systems and Intelligent Control Conference. The conference is devoted primarily to computer based feedback control systems that rely on rule bases, machine learning, and other artificial intelligence and soft computing techniques. The theme of the 1993 conference was "Fuzzy Logic, Neural Networks, and Soft Computing." Thomas L. Ward Institution for Fuzzy Systems and Intelligent Control, Inc. P. O. Box 1297 Louisville KY 40201-1297 USA Phone: +1.502.588.6342 Fax: +1.502.588.5633 Email: TLWard01@ulkyvm.louisville.edu, TLWard01@ulkyvm.bitnet INTERNATIONAL FUZZY SYSTEMS ASSOCIATION (IFSA) Holds biannual conferences that rotate between Asia, North America, and Europe. Membership is $232, which includes a subscription to the International Journal of Fuzzy Sets and Systems. Prof. Philippe Smets University of Brussels, IRIDIA 50 av. F. Roosevelt CP 194/6 1050 Brussels, Belgium LABORATORY FOR INTERNATIONAL FUZZY ENGINEERING (LIFE) Laboratory for International Fuzzy Engineering Research Siber Hegner Building 3FL 89-1 Yamashita-cho, Naka-ku Yokohama-shi 231 Japan Email: @fuzzy.or.jp NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY (NAFIPS) Holds a conference and a workshop in alternating years. President: Dr. Jim Keller President NAFIPS Electrical & Computer Engineering Dept University of Missouri-Col Columbia, MO 65211 USA Phone +1.314.882.7339 Email: ecejk@mizzou1.missouri.edu, ecejk@mizzou1.bitnet Secretary/Treasurer: Thomas H. Whalen Sec'y/Treasurer NAFIPS Decision Sciences Dept Georgia State University Atlanta, GA 30303 USA Phone: +1.404.651.4080 Email: qmdthw@gsuvm1.gsu.edu, qmdthw@gsuvm1.bitnet SPANISH ASSOCIATION FOR FUZZY LOGIC AND TECHNOLOGY Prof. J. L. Verdegay Dept. of Computer Science and A.I. Faculty of Sciences University of Granada 18071 Granada (Spain) Phone: +34.58.244019 Tele-fax: +34.58.243317, +34.58.274258 Email: jverdegay@ugr.es ================================================================ Subject: [19] Companies Supplying Fuzzy Tools Date: 15-APR-93 Adaptive Informations Systems: This is a new company that specializes in fuzzy information systems. Main products of AIS: - Consultancy and application development in fuzzy information retrieval and flexible querying systems - Development of a fuzzy querying application for value added network services - A fuzzy solution for utilization of a large (lexicon based) terminological knowledge base for NL query evaluation Adaptive Informations Systems Hoestvej 8 B DK-2800 Lyngby Denmark Phone: 45-4587-3217 Email: hll@dat.ruc.dk American NeuraLogix: Products: NLX110 Fuzzy Pattern Comparator. NLX230 8-bit single-chip fuzzy microcontroller. NLX20xC 8- and 16-bit VLSI Core elements for fuzzy processing. Others Other nonfuzzy and quasi-fuzzy devices. [American NeuraLogix describes these chips and cores as "fuzzy" processing devices, but as far as I can tell, they're not really fuzzy. The NLX110 is a Hamming-distance calculator, and the NLX230 and NLX20xC are based on a winner-take-all inference strategy that discards most of the advantages of fuzzy expert systems. Read the data sheets carefully before deciding.] American NeuraLogix, Inc. 411 Central Park Drive Sanford, FL 32771 USA Phone: 407-322-5608 Fax: 407-322-5609 Aptronix: Products: Fide A MS Windows-hosted graphical development environment for fuzzy expert systems. Code generators for Motorola's 6805, 68HC05, and 68HC11, and Omron's FP-3000 are available. A demonstration version of Fide is available. Aptronix, Inc. 2150 North First Street, Suite 300 San Jose, Ca. 95131 USA Phone: 408-428-1888 Fax: 408-428-1884 Fuzzy Net BBS: 408-428-1883, 8/n/1 ByteCraft, Ltd.: Products: Fuzz-C "A C preprocessor for fuzzy logic" according to the cover of its manual. Translates an extended C language to C source code. Byte Craft Limited 421 King Street North Waterloo, Ontario Canada N2J 4E4 Phone: 519-888-6911 Fax: 519-746-6751 Support BBS: 519-888-7626 Fujitsu: Products: MB94100 Single-chip 4-bit (?) fuzzy controller. FuziWare: Products: FuziCalc An MS-Windows-based fuzzy development system based on a spreadsheet view of fuzzy systems. FuziWare, Inc. 316 Nancy Kynn Lane, Suite 10 Knoxville, Tn. 37919 USA Phone: 800-472-6183, 615-588-4144 Fax: 615-588-9487 FuzzySoft AG: Product: FuzzySoft Fuzzy Logic Operating System runs under MS-Windows, generates C-code, extended simulation capabalities. Selling office for Germany, Switzerland and Austria (all product inquiries should be directed here) GTS Trautzl GmbbH Gottlieb-Daimler-Str. 9 W-2358 Kaltenkirchen/Hamburg Germany Phone: (49) 4191 8711 Fax: (49) 4191 88665 Fuzzy Systems Engineering: Products: Manifold Editor ? Manifold Graphics Editor ? [These seem to be membership function & rulebase editors.] Fuzzy Systems Engineering P. O. Box 27390 San Diego, CA 92198 USA Phone: 619-748-7384 Fax: 619-748-7384 (?) HyperLogic, Inc.: Products: CubiCalc An MS-Windows-based fuzzy development environment. CubiCalc RTC C source-code generator addon for CubiCalc. HyperLogic Corp 1855 East Valley Parkway, Suite 210 P. O. Box 3751 Escondido, Ca. 92027 USA Phone: 619-746-2765 Fax: 619-746-4089 Inform: Products: fuzzyTECH 3.0 A graphical fuzzy development environment. Versions are available that generate either C source code or Intel MCS-96 assembly source code as output. A demonstration version is available. Runs under MS-DOS. Inform Software Corp 1840 Oak Street, Suite 324 Evanston, Il. 60201 USA Phone: 708-866-1838 INFORM GmbH Geschaeftsbereich Fuzzy--Technologien Pascalstraese 23 W-5100 Aachen Tel: (02408) 6091 Fax: (02408) 6090 Metus Systems Group: Products: Metus Fuzzy Library A library of fuzzy processing routines for C or C++. Source code is available. The Metus Systems Group 1 Griggs Lane Chappaqua, Ny. 10514 USA Phone: 914-238-0647 Modico: Products: Fuzzle 1.8 A fuzzy development shell that generates either ANSI FORTRAN or C source code. Modico, Inc. P. O. Box 8485 Knoxville, Tn. 37996 USA Phone: 615-531-7008 Oki Electric: Products: MSM91U111 A single-chip 8-bit fuzzy controller. Europe: Oki Electric Europe GmbH. Hellersbergstrasse 2 D-4040 Neuss, Germany Phone: 49-2131-15960 Fax: 49-2131-103539 Hong Kong: Oki Electronics (Hong Kong) Ltd. Suite 1810-4, Tower 1 China Hong Kong City 33 Canton Road, Tsim Sha Tsui Kowloon, Hong Kong Phone: 3-7362336 Fax: 3-7362395 Japan: Oki Electric Industry Co., Ltd. Head Office Annex 7-5-25 Nishishinjuku Shinjuku-ku Tokyo 160 JAPAN Phone: 81-3-5386-8100 Fax: 81-3-5386-8110 USA: Oki Semiconductor 785 North Mary Avenue Sunnyvale, Ca. 94086 USA Phone: 408-720-1900 Fax: 408-720-1918 OMRON Corporation: Products: C500-FZ001 Fuzzy logic processor module for Omron C-series PLCs. E5AF Fuzzy process temperature controller. FB-30AT FP-3000 based PC AT fuzzy inference board. FP-1000 Digital fuzzy controller. FP-3000 Single-chip 12-bit digital fuzzy controller. FP-5000 Analog fuzzy controller. FS-10AT PC-based software development environment for the FP-3000. Japan Kazuaki Urasaki Fuzzy Technology Business Promotion Center OMRON Corporation 20 Igadera, Shimokaiinji Nagaokakyo Shi, Kyoto 617 Japan Phone: 81-075-951-5117 Fax: 81-075-952-0411 USA Sales (all product inquiries should be directed here) Pat Murphy OMRON Electronics, Inc. One East Commerce Drive Schaumburg, IL 60173 USA Phone: 708-843-7900 Fax: 708-843-7787/8568 USA Research Satoru Isaka OMRON Advanced Systems, Inc. 3945 Freedom Circle, Suite 410 Santa Clara, CA 95054 Phone: 408-727-6644 Fax: 408-727-5540 Email: isaka@oas.omron.com Togai InfraLogic, Inc.: Togai InfraLogic (TIL for short) supplies software development tools, board-, chip- and core-level fuzzy hardware, and engineering services. Contact info@til.com for more detailed information. Products: FC110 (the FC110(tm) Digital Fuzzy Processor (DFP-tm)). An 8-bit microprocessor/coprocessor with fuzzy acceleration. FC110DS (the FC110 Development System) A software development package for the FC110 DFP, including an assembler, linker and Fuzzy Programming Language (FPL-tm) compiler. FCA VLSI Cores based on Fuzzy Computational Acceleration (FCA-tm). FCA10AT FC110-based fuzzy accelerator board for PC/AT-compatibles. FCA10VME FC110-based four-processor VME fuzzy accelerator. FCD10SA FC110-based fuzzy processing module. FCD10SBFC FC110-based single board fuzzy controller module. FCD10SBus FC110-based two-processor SBus fuzzy accelerator. FCDS (the Fuzzy-C Development System) An FPL compiler that emits K&R or ANSI C source to implement the specified fuzzy system. MicroFPL An FPL compiler and runtime module that support using fuzzy techniques on small microcontrollers by several companies. TILGen A tool for automatically constructing fuzzy expert systems from sampled data. TILShell+ A graphical development and simulation environment for fuzzy systems. USA Togai InfraLogic, Inc. 5 Vanderbilt Irvine, CA 92718 USA Phone: 714-975-8522 Fax: 714-975-8524 Email: info@til.com Toshiba: Products: T/FC150 10-bit fuzzy inference processor. LFZY1 FC150-based NEC PC fuzzy logic board. T/FT Fuzzy system development tool. TransferTech GmbH: Products: Fuzzy Control Manager (FMC) Fuzzy shell, runs under MS-Windows TransferTech GmbH. Rebenring 33 W-3300 Braunschweig, Germany Phone: 49-531-3801139 Fax: 49-531-3801152 ================================================================ Subject: [20] Fuzzy Researchers Date: 15-APR-93 This is a *partial* list of some of the researchers and research organizations in the field of fuzzy logic and fuzzy expert systems. A list of "Who's Who in Fuzzy Logic" may be obtained by sending a message to listserver@vexpert.dbai.tuwien.ac.at with GET LISTSERVER WHOISWHOINFUZZY in the message body. New entries and corrections should be sent to Robert Fuller . Center for Fuzzy Logic and Intelligent Systems Research (Texas A&M): This group publishes a Technical Report Series, in addition to the proceedings and video tapes of the first and second International Workshop on Industrial Fuzzy Control and Intelligent Systems (IFIS 91/92). Dr. John Yen Center for Fuzzy Logic and Intelligent Systems Research Texas A&M University MS 3112 Harvey R. Bright Building Texas A&M University College Station, TX 77843 USA Phone: 409-845-5466 Fax: 409-847-8578 Email: cfl@cs.tamu.edu German National Research Center for Computer Science (GMD): The GMD supports a fuzzy logic group which does research in - adaptive control - VLSI design - image processing Liliane E. Peters GMD-SET P. O. Box 1316 D-5205 St. Augustin 1, Germany Phone: 49-2241-14-2332 Fax: 49-2241-14-2342 Email: peters@borneo.gmd.de Swiss Federal Institute of Technology (SFIT): Email: stegmaier@ifr.ethz.ch, vestli@ifr.ethz.ch Tokyo Institute of TechUWVFP( FP0 ‹؋F&O F&G&G<ʋЋ™Ƌ$6&~&6&F&9EtL2pP3P F&EĞ&(6&>pt*6&>vt TF&EF,6&>~FvFĞ&^&9~Ì~ڹ-F*Ftz8ft F^&8Gui~t F^&8GuWF+F;F| F9Fu~tF@t8F^&8Gt,F+F;F~<.6&>t0^&GĞ&v&@F[FF,6&9~2F^&8Gu FuӋvzvĞ&7X06&?&&DPĞ&&& 26&>|u 46&z66&^&Gȋƻ[؎86&>&I86&&a~t;F*PĞ&&& 46&zF*Pv:6&& ud26&>|u 46&zĞ&&& <6&6&6` P, >6&F&H t8PP  |FPPh P:6&6&6P  |FPPh P*6&>vt B6-4127, Fax +49(6151)293445 email: bmpf@irt1.rt.e-technik.th-darmstadt.de ================================================================ ;;; *EOF*