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.
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© 2005 Springer-Verlag Berlin Heidelberg
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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
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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
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