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RETRACTED ARTICLE: Nonlinear dynamic systems identification based on dynamic wavelet neural units

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This article was retracted on 01 February 2011

This article was retracted on 01 February 2011

Abstract

In this paper, a dynamic wavelet network (DWN) is proposed and applied to identify black box models of the process. The well-known delta-rule is extended to the dynamic delta-rule in order to optimize wavelet network parameters. A chemical process was chosen as a realistic nonlinear system to demonstrate the identification performance. A comparison was made between the approach presented in this paper and dynamic multi layer perceptron neural networks.

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Author information

Authors and Affiliations

  1. Department of Physics, Faculty of Sciences, University M’hamed Bougara, Boumerdès, Algeria

    L. Saad Saoud

  2. Department of Electronic, Faculty of Engineering, University Ferhat Abbas, Sétif, Algeria

    A. Khellaf

Authors
  1. L. Saad Saoud
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  2. A. Khellaf
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Corresponding author

Correspondence to L. Saad Saoud.

Additional information

This article has been retracted due to plagiarism.

An erratum to this article can be found at http://dx.doi.org/10.1007/s00521-011-0520-y

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Saad Saoud, L., Khellaf, A. RETRACTED ARTICLE: Nonlinear dynamic systems identification based on dynamic wavelet neural units. Neural Comput & Applic 19, 997–1002 (2010). https://doi.org/10.1007/s00521-010-0438-9

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  • DOI: https://doi.org/10.1007/s00521-010-0438-9

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