Abstract
The paper concerns the simultaneous optimization for structure and parameters of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. HFCGA is used to optimize structure and parameters of ANFIS-based fuzzy model simultaneously. The granulation is realized with the aid of the C-means clustering. Through the simultaneous optimization mechanism to be explored, we can find the overall optimal values related to structure as well as parameter identification of ANFIS-based fuzzy model via HFCGA, C-Means clustering and standard least square method. A comparative analysis demon-strates that the proposed algorithm is superior to the conventional methods.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tong, R.M.: Synthesis of Fuzzy Models for Industrial Processes. Int. J. Gen. Syst. 4, 143–162 (1978)
Pedrycz, W.: An Identification Algorithm in Fuzzy Relational System. Fuzzy Sets Syst. 13, 153–167 (1984)
Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans. Syst, Cybern. 15(1), 116–132 (1985)
Oh, S.K., Pedrycz, W.: Identification of Fuzzy Systems by Means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems. Fuzzy Sets and Syst. 115(2), 205–230 (2000)
Pderycz, W., Vukovich, G.: Granular Neural Networks. Neurocomputing 36, 205–224 (2001)
Krishnaiah, P.R., Kanal, L.N. (eds.): Classification, Pattern Recognition, and Reduction of Dimensionality. Handbook of Statistics, vol. 2. North-Holland, Amsterdam (1982)
Lin, S.C., Goodman, E., Punch, W.: Coarse-Grain Parallel Genetic Algorithms: Categorization and New Approach. In: IEEE Conf. on Parallel and Distrib. Processing (Nov. 1994)
Hu, J.J., Goodman, E.: The Hierarchical Fair Competition (HFC) Model for Parallel Evolutionary Algorithms. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC2002, Honolulu. Hawaii, IEEE Computer Society Press, Los Alamitos (2002)
Lyu, M.R.: Handbook of Software Reliability Engineering, pp. 510–514. McGraw-Hill, New York (1995)
Park, H.S., Oh, S.K.: Fuzzy Relation-Based Fuzzy Neural-Networks using a Hybrid Identification Algorithm. International Journal of Control, Automation, and Syst. 1(3), 289–300 (2003)
Park, H.S., Oh, S.K.: Multi-FNN Identification Based on HCM Clustering and Evolutionary Fuzzy Granulation. International Journal of Control, Automation, and Syst. 1(2), 194–3202 (2003)
Oh, S.K., Pedrycz, W.: A New Approach to Self-Organizing Multi-Layer Fuzzy Polynomial Neural Networks Based on Genetic Optimization. Advanced Engineering Informatics 18, 29–39 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Choi, JN., Oh, SK., Seo, KS. (2007). Simultaneous Optimization of ANFIS-Based Fuzzy Model Driven to Data Granulation and Parallel Genetic Algorithms. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-72395-0_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
eBook Packages: Computer ScienceComputer Science (R0)