Optimization of Information Granulation-Oriented Fuzzy Set Model Using Hierarchical Fair Competition-Based Parallel Genetic Algorithms

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on information granulation and Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. 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 and HCM method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

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

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

[3]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[4]  C. James Li,et al.  Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks , 1999 .

[5]  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..

[6]  T. Martin McGinnity,et al.  Predicting a Chaotic Time Series using Fuzzy Neural network , 1998, Inf. Sci..

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

[8]  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).

[9]  Sung-Kwun Oh,et al.  Fuzzy Relation-Based Fuzzy Neural-Networks Using a Hybrid Identification Algorithm , 2003 .

[10]  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.

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

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

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

[14]  R. Scott Crowder,et al.  Predicting the Mackey-Glass Timeseries With Cascade-Correlation Learning , 1990 .