Identification of Fuzzy Models Using Cartesian Genetic Programming

Fuzzy models have capability for solving problem in different application such as pattern recognition, prediction and control. Nevertheless, it has to be emphasized that the identification of a fuzzy model is complex task with many local minima. Cartesian genetic programming provides a way to solve such complex optimization problem. In this paper, fuzzy model is in form of network. Cartesian genetic programming is used to optimize the antecedent part and recursive least square is used to optimized the consequent part. The initialization of membership function parameters are doing with fuzzy clustering. Benefit of the methodology is illustrated by simulation results.

[1]  Francisco Herrera,et al.  Genetic Fuzzy Systems: Status, Critical Considerations and Future Directions , 2005 .

[2]  Mu-Song Chen,et al.  A comparative study of learning methods in tuning parameters of fuzzy membership functions , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[3]  Julian Francis Miller,et al.  Redundancy and computational efficiency in Cartesian genetic programming , 2006, IEEE Transactions on Evolutionary Computation.

[4]  Kim-Fung Man,et al.  Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction , 2005, Fuzzy Sets Syst..

[5]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

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

[7]  Rui Pedro Paiva,et al.  Interpretability and learning in neuro-fuzzy systems , 2004, Fuzzy Sets Syst..

[8]  Andreas Bastian.  Identifying fuzzy models utilizing genetic programming , 2000, Fuzzy Sets Syst..

[9]  Julian Francis Miller,et al.  Cartesian genetic programming , 2000, GECCO '10.

[10]  Julian F. Miller,et al.  What bloat? Cartesian Genetic Programming on Boolean problems , 2003 .

[11]  John R. Koza,et al.  Genetic generation of both the weights and architecture for a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[12]  Bernhard Sendhoff,et al.  On generating FC3 fuzzy rule systems from data using evolution strategies , 1999, IEEE Trans. Syst. Man Cybern. Part B.