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The Study of the Effectiveness Using the Expanded Neural Network in System Identification

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3214))

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Abstract

We have proposed the expanded neural network which the noise model has incorporated into the output layer of the neural network. The expanded neural network is able to apply to the output error model for the identification of a nonlinear system. In this paper, we consider whether the expanded neural network is able to apply effectively to estimate the nonlinear system that has a system noise. It is shown that the estimated accuracy is improved with the included noise model also in this case from the simulation.

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References

  1. Chen, S., Billings, S.A., Grant, P.M.: Non-linear system identification using neural networks. INT. J. CONTROL 51(6), 1191–1214 (1990)

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  3. Yamawaki, S.: A System Identification Method using Expanded Neural Networks. In: Knowledge-Based Intelligent Information Engineering System & Allied Technologies, KES 2002, pp. 358–363. IOS Press, Amsterdam (2002)

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  4. Yamawaki, S., Lakhmi, J.: Expanded Neural Networks in System Identification. In: Knowledge-Based Intelligent Information Engineering System & Allied Technologies, KES 2003, pp. 1116–1121. IOS Press, Amsterdam (2003)

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  5. Yamawaki, S., Lakhmi, J.: Robust System Identification using Neural Networks; Knowledge-Based Intelligent Information Engineering System & Allied Technologies, KES 2004, (2004)

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Author information

Authors and Affiliations

  1. Department of Electric and Electronic Engineering, School of Science and Engineering, Kinki University, Osaka, 577-8502, Japan

    Shigenobu Yamawaki

  2. Knowledge-Based Intelligent Engineering Systems Centre (KES), University of South Australia, Adelaide, Mawson Lakes, 5095, South Australia

    Lakhmi Jain

Authors
  1. Shigenobu Yamawaki
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  2. Lakhmi Jain
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Yamawaki, S., Jain, L. (2004). The Study of the Effectiveness Using the Expanded Neural Network in System Identification. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_125

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  • DOI: https://doi.org/10.1007/978-3-540-30133-2_125

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23206-3

  • Online ISBN: 978-3-540-30133-2

  • eBook Packages: Springer Book Archive

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