Abstract
This paper proposes a time series prediction method for the nonlinear system using the fuzzy system and the genetic algorithm. At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and the input differences, a better time prediction series system may be obtained. In addition, we may obtain the optimal fuzzy membership functions in terms of the evolutionary strategy and we obtain the time series prediction methods using the optimal fuzzy rules. We compare the time series prediction method using the genetic algorithm with that using the evolutionary strategy.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kim, I., Gong, C.W.: The Fuzzy Learning Algorithms for Time Series Prediction. The fuzzy and intelligent system journal 7(3), 34–42 (1997)
Tong, R.M.: The evaluation of Fuzzy Models derived from Experimental Data. Fuzzy sets and Systems 4, 1–12 (1980)
Pedrycz, W.: Fuzzy Control & Fuzzy Systems. John Wiley & Sons, Chichester (1989)
Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedback Networks are Universal Approximators. Neural Network 2, 359–366 (1989)
Wang, L.E.: Fuzzy Systems are Universal Approximators. In: Proc. IEEE International Conf. on Fuzzy Systems, San Diego, pp. 1163–1170 (1992)
Wang, L.X., Mendel, J.M.: Generating Fuzzy Rules by Learning from Examples. IEEE Trans Syst., Man, Cybern. 22, 1414–1427 (1992)
Jang, J.R., Sun, C.: Prediction Chaotic Time Series with Fuzzy If_Then rules. In: Second IEEE International Conference on Fuzzy Systems, San Francisco, pp. 1079–1084 (1993)
Ye, Z., Gu, L.: A Fuzzy System for Trading the Shanghai Stock Market. In: Deboeck, G.J. (ed.) Trading on the Edge, Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets, pp. 207–214. Wiley, New York (1994)
Benachenhou, D.: Smart Trading with (FRET). In: Deboeck, G.J. (ed.) Trading on the Edge, Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets, pp. 215–242. Wiley, New York (1994)
Lee, S.R., Kim, I.: Study on New Fuzzy Time Series Prediction Method. Myongji University Technical journal 19, 565–569 (1999)
Michalewicz, Z.: Genetic Algorithms+ Data Structures=Evolution Programs. Springer, Heidelberg (1994)
do Lim, Y., Lee, S.D.: Fuzzy, Neural Networks and the Evolutionary evolution, pp. 126–215. Young and il publishing company, Seoul (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, H.I. (2005). A Fuzzy Time Series Prediction Method Using the Evolutionary Algorithm. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_55
Download citation
DOI: https://doi.org/10.1007/11538356_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28227-3
Online ISBN: 978-3-540-31907-8
eBook Packages: Computer ScienceComputer Science (R0)