Abstract
This paper proposes a recurrent wavelet-based neuro-fuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA), performs the structure/ parameter learning for dynamically constructing the RWNFS. That is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. An illustrative example was conducted to show the performance and applicability of the proposed R-HELA method.
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References
Lin, C.T., Lee, C.S.G.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent System. Prentice-Hall, Englewood Cliffs (1996)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man, Cybern. 15, 116–132 (1985)
Lin, C.J., Chin, C.C.: Prediction and identification using wavelet-based recurrent fuzzy neural networks. IEEE Trans. on Systems, Man, and Cybernetics (Part:B) 34, 2144–2154 (2004)
Fogel, L.J.: Evolutionary programming in perspective: The top-down view. In: Zurada, J.M., Marks II, R.J., Goldberg, C. (eds.) Computational Intelligence: Imitating Life, IEEE Press, Piscataway, NJ (1994)
Karr, C.L.: Design of an adaptive fuzzy logic controller using a genetic algorithm. In: Proc. The Fourth Int. Conf. Genetic Algorithms, pp. 450–457 (1991)
Lin, C.T., Jou, C.P.: GA-based fuzzy reinforcement learning for control of a magnetic bearing system. IEEE Trans. Syst. Man, Cybern. Part B 30, 276–289 (2000)
Juang, C.F., Lin, J.Y., Lin, C.T.: Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Trans. Syst. Man, Cybern. Part B 30, 290–302 (2000)
Bandyopadhyay, S., Murthy, C.A., Pal, S.K.: VGA-classfifer: design and applications. IEEE Trans s. Syst. Man, and Cyber. Part B 30, 890–895 (2000)
Juang, C.F.: Combination of online clustering and Q-value based GA for reinforcement fuzzy system design. IEEE Trans. Fuzzy Systems 13, 289–302 (2005)
Michalewicz, Z.: Genetic Algorithms+Data Structures=Evolution Programs. Springer, New York (1999)
Tanese, R.: Distributed genetic algorithm. In: Proc. Int. Conf. Genetic Algorithms, pp. 434–439 (1989)
Arabas, J., Michalewicz, Z., Mulawka, J.: GAVaPS-A genetic algorithm with varying population size. In: Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 73–78 (1994)
Moriarty, D.E., Miikkulainen, R.: Efficient reinforcement learning through symbiotic evolution. Mach. Learn. 22, 11–32 (1996)
Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evolutionary Computation, 450–457 (1991)
Lee, K.Y., Bai, X., Park, Y.-M.: Optimization method for reactive power planning by using a modified simple genetic algorithm. IEEE Trans. Power Systems 10, 1843–1850 (1995)
Cordon, O., Herrera, F., Hoffmann, F., Magdalena, L.: Advances in Fuzzy Systems-Applications and Theory. World Scientific Publishing, NJ (2001)
Whitley, D., Dominic, S., Das, R., Anderson, C.W.: Genetic reinforcement learning for neuro control problems. Mach. Learn. 13, 259–284 (1993)
Wang, H., Kwong, S., Jin, Y., Wei, W., Man, K.F.: Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets and Systems 149, 149–186 (2005)
Ho, D.W.C., Zhang, P.A., Xu, J.: Fuzzy wavelet networks for function learning. IEEE Trans. Fuzzy Systs. 9, 200–211 (2001)
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Chen, CH., Lin, CJ., Lee, CY. (2007). Efficient Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neuro-fuzzy Systems. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_21
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DOI: https://doi.org/10.1007/978-3-540-73325-6_21
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