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Robust System Identification Using Neural Networks

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

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3213))

Abstract

The robust system identification method using the neural network is developed based on the canonical variate analysis (CVA). The main contribution of this algorithm is using CVA to obtain the k-step optimal prediction value. Therefore, the method to obtain the comparatively accurate estimate is introduced without iteration calculations. We show that this algorithm can be applied to successfully identify the nonlinear system in the presence of comparatively loud noise. Results from several simulation studies have been included to the effectiveness of this method.

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References

  1. Larimore, W.E.: Canonical variate analysis in identification, filtering, and adaptive control. In: Proc. 29th CDC, vol. 596, pp. 604 (1990)

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  5. Yamawaki, S., Lakhmi, J.: Expanded Neural Networks in System Identification. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 1116–1121. Springer, Heidelberg (2003)

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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, 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). Robust System Identification Using Neural Networks. 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 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_107

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  • DOI: https://doi.org/10.1007/978-3-540-30132-5_107

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23318-3

  • Online ISBN: 978-3-540-30132-5

  • eBook Packages: Springer Book Archive

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