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A hybrid evolutionary learning algorithm for TSK-type fuzzy model design. (English) Zbl 1145.93371
Summary: In this paper, a TSK-type Fuzzy Model (TFM) with a Hybrid Evolutionary Learning Algorithm (HELA) is proposed. The proposed HELA method combines the Compact Genetic Algorithm (CGA) and the modified variable-length genetic algorithm. Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In the proposed HELA method, individuals of the same length constitute the same group, and there are multiple groups in a population. Moreover, the proposed HELA adopts the CGA to carry out the elite-based reproduction strategy. The CGA represents a population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA. The evolution processes of a population consist of three major operations: group reproduction using the compact genetic algorithm, variable two-part individual crossover, and variable two-part mutation. Computer simulations have demonstrated that the proposed HELA method gives a better performance than some existing methods.

MSC:
93C42Fuzzy control systems
90C59Approximation methods and heuristics
68T05Learning and adaptive systems
Software:
Genocop; ANFIS
WorldCat.org
Full Text: DOI
References:
[1] Lin, C. T.; Lee, C. S. G.: Neural fuzzy systems: A neuro-fuzzy synergism to intelligent system. (1996)
[2] Towell, G. G.; Shavlik, J. W.: Extracting refined rules from knowledge-based neural networks. Mach learn. 13, 71-101 (1993)
[3] Lin, C. J.; Lin, C. T.: An ART-based fuzzy adaptive learning control network. IEEE trans. Fuzzy syst. 5, No. 4, 477-496 (1997)
[4] Wang, L. X.; Mendel, J. M.: Generating fuzzy rules by learning from examples. IEEE trans. Syst. man cybern. 22, No. 6, 1414-1427 (1992)
[5] Takagi, T.; Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE trans. Syst. man cybern. 15, 116-132 (1985) · Zbl 0576.93021
[6] Jang, J. -S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE trans. Syst. man cybern. 23, 665-685 (1993)
[7] Juang, C. F.; Lin, C. T.: An self-constructing neural fuzzy inference network and its applications. IEEE trans. Fuzzy syst. 6, No. 1, 12-31 (1998)
[8] Lin, F. J.; Lin, C. H.; Shen, P. H.: Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive. IEEE trans. Fuzzy syst. 9, No. 5, 751-759 (2001)
[9] Takagi, H.; Suzuki, N.; Koda, T.; Kojima, Y.: Neural networks designed on approximate reasoning architecture and their application. IEEE trans. Neural netw. 3, No. 5, 752-759 (1992)
[10] Bäck, T.; Schwefel, H. P.: An overview of evolutionary algorithms for parameter optimization. Evol. comput. 1, No. 1, 1-23 (1993)
[11] Fogel, D. B.: Evolutionary computation: toward a new philosophy of machine intelligence. (1995) · Zbl 0926.68052
[12] Yao, X.: Evolutionary computation: theory and applications. (1999)
[13] Goldberg, D. E.: Genetic algorithms in search optimization and machine learning. (1989) · Zbl 0721.68056
[14] Koza, J. K.: Genetic programming: on the programming of computers by means of natural selection. (1992) · Zbl 0850.68161
[15] Fogel, L. J.: Evolutionary programming in perspective: the top-down view. Computational intelligence: imitating life (1994)
[16] Rechenberg, I.: Evolution strategy. Computational intelligence: imitating life (1994)
[17] C.L. Karr, Design of an adaptive fuzzy logic controller using a genetic algorithm, in: Proc. Fourth Int. Conf. Genetic Algorithms, 1991, pp. 450--457
[18] Homaifar, A.; Mccormick, E.: Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE trans. Fuzzy syst. 3, No. 2, 129-139 (1995)
[19] M. Lee, H. Takagi, Integrating design stages of fuzzy systems using genetic algorithms, in: Proc. 2nd IEEE Int. Conf. Fuzzy Systems, San Francisco, CA, 1993, pp. 612--617
[20] Belarbi, K.; Titel, F.: Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach. IEEE trans. Fuzzy syst. 8, No. 4, 398-405 (2000)
[21] Juang, C. F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE trans. Syst. man cybern. B 34, No. 2, 997-1006 (2004)
[22] R. Eberchart, J. Kennedy, A new optimizer using particle swarm theory, in: Proc. of 6th Int. Sym. on Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39--43
[23] Kennedy, J.; Eberhart, R.: Particle swarm optimization. Proc. of IEEE int. Conf. on neural networks, 1942-1948 (1995)
[24] Bandyopadhyay, S.; Murthy, C. A.; Pal, S. K.: VGA-classifier: design and applications. IEEE trans. Syst. man cyber. B 30, 890-895 (2000)
[25] Michalewicz, Z.: Genetic algorithms+Data structures=Evolution programs. (1999) · Zbl 0841.68047
[26] J. Arabas, Z. Michalewicz, J. Mulawka, GAVaPS--A genetic algorithm with varying population size, in: Proc. IEEE Int. Conf. on Evolutionary Computation, Orlando, 1994, pp. 73--78
[27] R. Tanese, Distributed genetic algorithm, in: Proc. Int. Conf. Genetic Algorithms, 1989, pp. 434--439
[28] R.J. Collins, D.R. Jefferson, Selection in massively parallel genetic algorithms, in: Proc. Int. Conf. Genetic Algorithms, 1991, pp. 249--256
[29] Moriarty, D. E.; Miikkulainen, R.: Efficient reinforcement learning through symbiotic evolution. Mach learn. 22, 11-32 (1996)
[30] Harik, G. R.; Lobo, F. G.; Goldberg, D. E.: The compact genetic algorithm. IEEE trans. Evol. comput. 3, No. 4, 287-297 (1999)
[31] Lee, K. Y.; Bai, Xiaomin; Park, Y. M.: Optimization method for reactive power planning by using a modified simple genetic algorithm. IEEE trans. Power syst. 10, No. 4, 1843-1850 (1995)
[32] Juang, C. F.; Lin, J. Y.; Lin, C. T.: Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE trans. Syst. man cybern. B 30, No. 2, 290-302 (2000)
[33] Cordon, O.; Herrera, F.; Hoffmann, F.; Magdalena, L.: Genetic fuzzy systems evolutionary tuning and learning of fuzzy knowledge bases. Advances in fuzzy systems--applications and theory 19 (2001) · Zbl 1042.68098
[34] Narendra, K. S.; Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE trans. Neural netw. 1, No. 1, 4-27 (1990)
[35] Tanomaru, J.; Omatu, S.: Process control by self-constructing trained neural controllers. IEEE trans. Ind. electron. 39, 511-521 (1992)
[36] Lin, C. -J.; Tsai, C. -N.: Using context-sensitive neuro-fuzzy system for nonlinear system identification. J. chin. Inst. eng. 27, No. 1, 1-8 (2004)