Abstract
We discuss a new design of group method of data handling (GMDH)-type neural network using evolutionary algorithm. The performances of the GMDH-type network depend strongly on the number of input variables and order of the polynomials to each node. They must be fixed by designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems we employed evolutionary algorithms. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. The appropriate information of each node are evolved accordingly and tuned gradually throughout the GA iterations. By the simulation results, we can show that the proposed networks have good performance.
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References
Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybern. 1, 364–378 (1971)
Ivakhnenko, A.G., Ivakhnenko, N.A.: Long-term prediction by GMDH algorithms using the unbiased criterion and the balance-of-variables criterion. Sov. Automat. Contr. 7, 40–45 (1974)
Ivakhnenko, A.G., Ivakhnenko, N.A.: Long-term prediction by GMDH algorithms using the unbiased criterion and the balance-of-variables criterion, part 2. Sov. Automat. Contr. 8, 24–38 (1975)
Farlow, S.J.: Self-Organizing Methods in Modeling. In: GMDH Type-Algorithms. Marcel Dekker, New York (1984)
Oh, S.K., Pedrycz, W.: The design of self-organizing Polynomial Neural Networks. Inf. Sci. 141, 237–258 (2002)
Kim, D.W.: Evolutionary Design of Self-Organizing Polynomial Neural Networks. Master’s thesis Dept. Control Instrum. Wonkwang Univ (2002)
Box, G.E.P., Jenkins, F.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control, 3rd edn. Prentice Hall, Englewood Cliffs (1994)
Leski, J., Czogala, E.: A new artificial neural networks based fuzzy inference system with moving consequents in if-then rules and selected applications. Fuzzy Sets Syst. 108, 289–297 (1999)
Kang, S.J., Woo, C.H., Hwang, H.S., Woo, K.B.: Evolutionary Design of Fuzzy Rule Base for Nonlinear System Modeling and Control. IEEE Trans. Fuzzy Syst. 8 (2000)
Kim, E., Lee, H., Park, M., Park, M.: A simple identified Sugeno-type fuzzy model via double clustering. Inf. Sci. 110, 25–39 (1998)
Lin, Y., Cunningham III, G.A.: A new approach to fuzzy-neural modeling. IEEE Trans. Fuzzy Syst. 3, 190–197 (1995)
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Kim, D., Park, GT. (2005). GMDH-Type Neural Network Modeling in Evolutionary Optimization. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_79
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DOI: https://doi.org/10.1007/11504894_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
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