Template-Based Fuzzy Systems Modeling

A methodology is suggested here for the development of fuzzy systems models that is a combination of the AI-expert systems approach, with its heavy dependence on the use of expert knowledge, and the neural network-type systems building, with its emphasis on learning from data observations. We use expert-provided information in the form of template linguistic values to induce potential elemental rules for the knowledge base of the system model. We then introduce input-output observations into a simple learning mechanism to obtain weights characterizing the effect of each of the potential elemental rules on the overall systems model. The development of the learning mechanism is based on a representation of systems models combining fuzzy logic and Dempster-Shafer theory, which we previously introduced.

[1]  R. Yager.  Quasi-associative operations in the combination of evidence , 1987 .

[2]  J. Kacprzyk,et al.  Advances in the Dempster-Shafer theory of evidence , 1994 .

[3]  T. Takagi,et al.  A New Approach to Design of Fuzzy Controller , 1983 .

[4]  Hamid R. Berenji,et al.  Learning and tuning fuzzy logic controllers through reinforcements , 1992, IEEE Trans. Neural Networks.

[5]  Dimitar Filev,et al.  Unified structure and parameter identification of fuzzy models , 1993, IEEE Trans. Syst. Man Cybern..

[6]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[8]  Ronald R. Yager,et al.  Arithmetic and Other Operations on Dempster-Shafer Structures , 1986, Int. J. Man Mach. Stud..

[9]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[10]  J.-S.R. Jang.  Fuzzy controller design without domain experts , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[11]  R. Yager.  On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[12]  Ronald R. Yager,et al.  Reasoning with Uncertainty for Expert Systems , 1985, IJCAI.

[13]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[14]  M. Sugeno,et al.  Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .

[15]  Michio Sugeno,et al.  An introductory survey of fuzzy control , 1985, Inf. Sci..

[16]  Ronald R. Yager,et al.  Including probabilistic uncertainty in fuzzy logic controller modeling using Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[17]  Piero P. Bonissone,et al.  Automated fuzzy knowledge base generation and tuning , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[18]  Smets Ph.,et al.  Belief functions, Non-standard logics for automated reasoning , 1988 .

[19]  R. Tong.  SYNTHESIS OF FUZZY MODELS FOR INDUSTRIAL PROCESSES-SOME RECENT RESULTS , 1978 .

[20]  Philippe Smets,et al.  Belief Functions versus Probability Functions , 1988, IPMU.

[21]  M. Sugeno,et al.  Fuzzy modeling and control of multilayer incinerator , 1986 .

[22]  R. Yager,et al.  Identification of nonlinear systems by fuzzy models , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[23]  R. Yager,et al.  On the issue of defuzzification and selection based on a fuzzy set , 1993 .

[24]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[25]  R. Yager,et al.  Analysis of Flexible Structured Fuzzy Logic Controllers , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[26]  Jerry M. Mendel,et al.  Back-propagation fuzzy system as nonlinear dynamic system identifiers , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[27]  Philippe Smets.  Non-standard logics for automated reasoning , 1988 .

[28]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.