Genetic Algorithm for Fuzzy Neural Networks using Locally Crossover
Keywords:rules, figures, citation of papers, citation of books, examples
AbstractFuzzy feed-forward (FFNR) and fuzzy recurrent networks (FRNN) proved to be solutions for "real world problems". In the most cases, the learning algorithms are based on gradient techniques adapted for fuzzy logic with heuristic rules in the case of fuzzy numbers. In this paper we propose a learning mechanism based on genetic algorithms (GA) with locally crossover that can be applied to various topologies of fuzzy neural networks with fuzzy numbers. The mechanism is applied to FFNR and FRNN with L-R fuzzy numbers as inputs, outputs and weights and fuzzy arithmetic as forward signal propagation. The Î±-cuts and fuzzy biases are also taken into account. The effectiveness of the proposed method is proven in two applications: the mapping a vector of triangular fuzzy numbers into another vector of triangular fuzzy numbers for FFNR and the dynamic capture of fuzzy sinusoidal oscillations for FRNN.
S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998.
D.J. Dubois, H. Prade, Fuzzy Sets and Systems: Theory and Applications, Academic Press, 1980.
D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional, 1989.
J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, 1992.
O. Cordon, V. Herrera, M. Lozano, On the Combination of Fuzzy Logic and Evolutionary Computation: a short review and Bibliography, in: W. Pedrycz (Ed.), Evolutionary Computation, Kluwer Academic Publishers, Dordrecht, pp.33-56, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_2
Y. Hayashi et. al., Fuzzy Control Rules in Convex Optimization, Fuzzy Neural Networks with Fuzzy Signals and Weights IJCNN'92, Vol. 2, pp. 165-195, 1992.
H. Ishibuchi, K. Kwon, H.Tanaka, A learning algorithm on fuzzy neural networks with triangular fuzzy weights, Fuzzy Sets and Systems, Vol. 72, No. 3, pp. 257-264, 1995. http://dx.doi.org/10.1016/0165-0114(94)00281-b
H. Ishibuchi, R. Fujioka, H.Tanaka, An Architecture of Neural Networks for Input Vectors of Fuzzy Numbers, Proc. FUZZ-IEEE '92, San Diego, USA, pp. 643-650, 1992. http://dx.doi.org/10.1109/fuzzy.1992.258597
H.N. Teodorescu, D. Arotaritei, E. Lopez Gonzales, A General Trail-and-Error Algorithm for Algebraic Fuzzy Neural Networks, Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, September 2-5, Vol. 1, pp. 8-12, 1996.
D. Arotaritei, Recurrent Algebraic Fuzzy Neural Networks based on Fuzzy Numbers, Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vol. 5, pp. 2676 - 2680, 2001.
R.A. Aliev, B. Fazlollahi, R.M. Vahidov, Genetic algorithm-based learning of fuzzy neural network, Part 1: feed-forward fuzzy neural networks, Fuzzy Sets and Systems, Vol. 118, Issue 2, pp. 351-358, 2001. http://dx.doi.org/10.1016/S0165-0114(98)00461-8
R. A. Aliev, R. R. Aliev, B. G. Guirimov, K. Uyar, Recurrent Fuzzy Neural Network Based System for Battery Charging, Lecture Notes in Computer Science, Vol. 4492, pp. 307-316, 2007. http://dx.doi.org/10.1007/978-3-540-72393-6_38
L.A. Zadeh, Fuzzy Sets, Inform. Control 8, pp. 338-353, 1965. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
R.J. Williams, D. Zipser, A learning algorithm for continually running fully recurrent neural networks, Neural Computation, Vol. 1, Issue 2, pp. 270-280, 1989. http://dx.doi.org/10.1162/neco.1918.104.22.1680
C. Chakraborty, D. Chakraborty, A theoretical development on a fuzzy distance measure for fuzzy numbers, Mathematical and Computer Modelling, Nr. 43, pp. 254-261, 2006. http://dx.doi.org/10.1016/j.mcm.2005.09.025
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