Two-Phase Identification of ANFIS-Based Fuzzy Systems with Fuzzy Set by Means of Information Granulation and Genetic Optimization
In this study, we propose the consecutive optimization of ANFIS-based fuzzy systems with fuzzy set. The proposed model formed by using respective fuzzy spaces (fuzzy set) implements system structure and parameter identification with the aid of information granulation and genetic algorithms. Information granules are sought as associated collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Information granulation realized with HCM clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise and the initial values of polynomial functions in the consequence. And the initial parameters are tuned with the aid of the genetic algorithms and the least square method. To optimally identify the structure and parameters we exploit the consecutive optimization of ANFIS-based fuzzy model by means of genetic algorithms. The proposed model is contrasted with the performance of conventional fuzzy models in the literature.
KeywordsGenetic Algorithm Membership Function Fuzzy System Fuzzy Model Information Granulation
Unable to display preview. Download preview PDF.
- 4.Sugeno, M., Yasukawa, T.: Linguistic Modeling Based on Numerical Data. In: IFSA 1991 Brussels, Computer, Management & System Science, pp. 264–267 (1991)Google Scholar
- 8.Krishnaiah, P.R., Kanal, L.N. (eds.): Classification, Pattern Recognition, and Reduction of Dimensionality. Volume 2 of Handbook of Statistics. North-Holland, Amsterdam (1982)Google Scholar
- 9.Golderg, D.E.: Genetic Algorithm in Search, Optimization & Machine Learning. Addison Wesley, Reading (1989)Google Scholar
- 18.Park, C.S., Oh, S.K., Pedrycz, W.: Fuzzy Identification by Means of Auto-Tuning Algorithm and Weighting Factor. In: The Third Asian Fuzzy Systems Symposium (AFSS), pp. 701–706 (1998)Google Scholar
- 20.Park, H.S., Oh, S.K.: Fuzzy Relation-based Fuzzy Neural-Networks Using a Hybrid Identification Algorithm. Int. J. of Control, Automations, and Systems 1(3), 289–300 (2003)Google Scholar