Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems
International Journal of Management and Fuzzy Systems
Volume 2, Issue 4, August 2016, Pages: 31-37
Received: Sep. 10, 2016;
Accepted: Oct. 29, 2016;
Published: Dec. 27, 2016
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Alireza Rezaee, Department of System and Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
This paper proposes a systematic fuzzy model (SFM) to control of general system. SFM model is parted to multiple parts such as: a single parameter in formulation of reasoning; a linear relationship between input and output as a result; the use of evolutionary programming for the selection of the appropriate system parameters and a fuzzy clustering algorithm. Unlike traditional methods of inference mechanism to select a priori reasoning mechanism; SFM model can adjust its parameters using evolutionary programming. To vary the degrees of linear functions of the fuzzy rules, a set of equations describes the system’s input and output locally. Thus, this model can take advantage of the properties of linear systems. Fuzzy rules, the fuzzy c- means clustering algorithm and proper selection of the cluster centers by using evolutionary algorithm have been investigated. Finally, this system has been tested and validated on both controlled robot arm joint.
Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems, International Journal of Management and Fuzzy Systems.
Vol. 2, No. 4,
2016, pp. 31-37.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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W. Pedrycz, fuzzy Sets Engineering. Boca Raton, FL: CRC, 1995.
B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: Prentice-Hall, 1992.
J. H. Nie and T. H. Lee, “Rule-based modeling: Fast construction and optimal manipulation,” IEEE Transactions on Systems, Man and Cybernetics, A, vol. 26, pp. 728-738, Nov. 1996.
R. Babuska and H. B. Verbruggen, “Fuzzy set methods for local modeling and identification,” in Multiple Model Approaches to Nonlinear Modeling and Control, R. Murray-Smith and T. A. Johansen, Eds. London, U.K.: Taylor& Francis, 1997, pp. 75-100.
M. Sugeno and T. Yasukawa, “A fuzzy-logic-based approach to qualitative modeling,” IEEE Transactions on Fuzzy Systems, 1, No. 1:7-31, 1993.
M. R. Emami, I. B. Turksen, A. A. Goldenberg, “An improved fuzzy modeling algorithm, part II: System identification,” NAFIPS, 1996.
K. Al-Sultan. A Tabu Search Approach to Clustering problems pattern recognition, Vol. 28, pp.1443-1451, 1995.
S. Chiu, “Method and software for extracting fuzzy classification rules by subtractive clustering,” NAFIPS, 1996.
A. Rezaee, “PSO for fuzzy goal programming,” Appl. Comput. Math, 5(2):218-226, 2006.
M. Setnes, R. Babuska, and H. B. Verbruggen. “Rule-based modeling: Precision and transparency.” IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 28(1): 165-169, 1998.
R. R. Yager and D. P. Filev, Essentials of Fuzzy Modeling and Control, John Wiley & Sons, Inc, 1994.
D. Dubois, H. Prade, Fuzzy Sets and Systems: Theory and Applications. Academic Press: New York, 1980.
X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, 87(9): 1423-1447, September 1999.
J.-S. R. Jang and N. Gulley, “The Fuzzy Logic Toolbox for Use with MATLAB,” The MathWorks, Inc., Natick, Massachusetts, 1995.