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Prediction of Time Sequence Using Recurrent Compensatory Neuro-fuzzy Systems

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

In this paper, a recurrent compensatory neuro-fuzzy system (RCNFS) is proposed for prediction of time sequence. The compensatory-based fuzzy reasoning method is using adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic systems more adaptive and effective. The recurrent network is embedded in the RCNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. Also, an on-line learning algorithm is proposed to automatically construct the RCNFS. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Information Engineering, Nankai Institute of Technology, Nantou County, 542, Taiwan, China

    ChiYung Lee

  2. Dept. of Computer Science and Information Engineering, Chaoyang University of Technology, 168 Gifeng E. Rd, Wufeng, Taichung County, 413, Taiwan, China

    ChengJian Lin

Authors
  1. ChiYung Lee
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  2. ChengJian Lin
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Lee, C., Lin, C. (2005). Prediction of Time Sequence Using Recurrent Compensatory Neuro-fuzzy Systems. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_100

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  • DOI: https://doi.org/10.1007/11427445_100

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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