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A Fuzzy Time Series Prediction Method Using the Evolutionary Algorithm

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Advances in Intelligent Computing (ICIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3645))

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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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Authors and Affiliations

  1. Dept. of Information Eng., Myongji University, Yongin Geonggi province, 449-728, Republic of Korea

    Hwan Il Kang

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  1. Hwan Il Kang
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Editors and Affiliations

  1. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. School of Computer & Information Technology, Beijing Jiaotong University, 100044, Beijing, P.R. China

    Xiao-Ping Zhang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

    Guang-Bin Huang

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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