Optimization of Fuzzy Model Driven to IG and HFC-Based GAs

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) and information data granulation. HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. The granulation is realized with the aid of the Hard C-means clustering (HCM). The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly organized based on center points of data group extracted by the HCM clustering. It concerns the fuzzy model-related parameters such as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the polynomial type of the consequence part of fuzzy rules. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

[1]  Erik D. Goodman,et al.  The hierarchical fair competition (HFC) model for parallel evolutionary algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[3]  Jianjun Hu,et al.  Adaptive Hierarchical Fair Competition (AHFC) Model For Parallel Evolutionary Algorithms , 2002, GECCO.

[4]  Sung-Kwun Oh,et al.  Design of Fuzzy Polynomial Neural Networks with the Aid of Genetic Fuzzy Granulation and Its Application to Multi-variable Process System , 2006, ISNN.

[5]  Sung-Kwun Oh,et al.  Self-organizing neurofuzzy networks in modeling software data , 2004, Fuzzy Sets Syst..

[6]  Jacek M. Zurada,et al.  Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part I , 2006, International Symposium on Neural Networks.

[7]  Antonio Bellacicco,et al.  Handbook of statistics 2: Classification, pattern recognition and reduction of dimensionality: P.R. KRISHNAIAH and L.N. KANAL (Eds.) North-Holland, Amsterdam, 1982, xxii + 903 pages, Dfl.275.00 , 1984 .

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

[9]  Sung-Kwun Oh,et al.  Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems , 2000, Fuzzy Sets Syst..

[10]  W. Pedrycz.  An identification algorithm in fuzzy relational systems , 1984 .

[11]  Witold Pedrycz,et al.  Granular neural networks , 2001, Neurocomputing.

[12]  Erik D. Goodman,et al.  Coarse-grain parallel genetic algorithms: categorization and new approach , 1994, Proceedings of 1994 6th IEEE Symposium on Parallel and Distributed Processing.

[13]  Laveen N. Kanal,et al.  Classification, Pattern Recognition and Reduction of Dimensionality , 1982, Handbook of Statistics.