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AFSS International Conference on Fuzzy Systems

AFSS 2002: Advances in Soft Computing — AFSS 2002 pp 260–266Cite as

Decomposed Neuro-fuzzy ARX Model

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2275))

Abstract

This paper explores a new approach for the modelling and identification of non-linear dynamic systems. A model, named the Decomposed Neuro- Fuzzy Auto-Regressive with eXogenous input model (DNFARX), based on decomposed structure of the fuzzy inference system, is proposed. An evolution of a neural network learning algorithm for the decomposed structure of the fuzzy inference system is suggested. A comparative study of the dynamic system modelling with conventional fuzzy inference system based models and the proposed model is presented for Box-Jenkins data set.

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Author information

Authors and Affiliations

  1. Institute of Automation, Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000, Maribor, Slovenia

    Marjan Golob & Boris Tovornik

Authors
  1. Marjan Golob
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  2. Boris Tovornik
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Editor information

Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203 B.T. Road, 700108, Calcutta, India

    Nikhil R. Pal

  2. Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Japan

    Michio Sugeno

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© 2002 Springer-Verlag Berlin Heidelberg

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Cite this paper

Golob, M., Tovornik, B. (2002). Decomposed Neuro-fuzzy ARX Model. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_35

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  • DOI: https://doi.org/10.1007/3-540-45631-7_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43150-3

  • Online ISBN: 978-3-540-45631-5

  • eBook Packages: Springer Book Archive

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