Evolutionary learning of fuzzy models

Abstract This paper presents an evolutionary algorithm for generating knowledge bases for fuzzy logic systems. The algorithm dynamically adjusts the focus of the genetic search by dividing the population into three sub-groups, each concerned with a different level of knowledge base optimisation. The algorithm was tested on the identification of two highly non-linear simulated plants. Such a task represents a challenging test for any learning technique and involves two opposite requirements, the exploration of a large high-dimensional search space and the achievement of the best modelling accuracy. The algorithm achieved learning results that compared favourably with those for alternative knowledge base generation methods.

[1]  Samir W. Mahfoud.  A Comparison of Parallel and Sequential Niching Methods , 1995, ICGA.

[2]  J. Liska,et al.  Complete design of fuzzy logic systems using genetic algorithms , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[3]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[4]  Duc Truong Pham,et al.  Evolutionary fuzzy logic system for intelligent fibre optic components assembly , 2002 .

[5]  Alexandre Parodi,et al.  A New Approach to Fuzzy Classifier Systems , 1993, ICGA.

[6]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[7]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[8]  M. G. Cooper,et al.  Genetic design of fuzzy controllers: the cart and jointed-pole problem , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[9]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[10]  David B. Fogel,et al.  Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence) , 2006 .

[11]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[12]  Chin-Teng Lin,et al.  Real-time supervised structure/parameter learning for fuzzy neural network , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[13]  Donald A. Waterman,et al.  Pattern-Directed Inference Systems , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Wei Gao.  Comparison study of genetic algorithm and evolutionary programming , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[15]  K. C. Ng,et al.  Design of sophisticated fuzzy logic controllers using genetic algorithms , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[16]  D. Pham,et al.  Outline of a new evolutionary algorithm for fuzzy systems learning , 2002 .

[17]  Roy George,et al.  Fuzzy clustering with genetic search , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[18]  H. Heider,et al.  Fuzzy system design with a cascaded genetic algorithm , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[19]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[20]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[21]  Russell C. Eberhart,et al.  Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

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

[23]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[24]  Abdollah Homaifar,et al.  Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[25]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[26]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[27]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[28]  Dilip Kumar Pratihar,et al.  Automatic design of fuzzy logic controller using a genetic algorithm—to predict power requirement and surface finish in grinding , 2004 .

[29]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[30]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[31]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[32]  Hideyuki Takagi,et al.  Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques , 1993, ICGA.

[33]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[34]  Yun Li,et al.  Genetic algorithms applied to fuzzy sliding mode controller design , 1995 .