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A New Approach Based on Recurrent Neural Networks for System Identification

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Computer and Information Sciences - ISCIS 2003 (ISCIS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2869))

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Abstract

This paper introduces a new approach based on artificial neural networks (ANNs) to identify a number of linear dynamic systems with single recurrent neural model. The structure of single neural model is capable of dealing with systems up to a given maximum number. Single recurrent neural model is trained by the backpropagation with momentum. Total nine systems from first to third orders have been used to validate the approach presented in this work. The results have shown that the recurrent single neural model is capable of identifying a number of systems successfully. The approach presented in this work provides simplicity, accuracy and compactness to system identification.

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Author information

Authors and Affiliations

  1. Engineering Faculty, Computer Engineering Department, Erciyes University, Kayseri, Turkey

    Adem Kalinli & Seref Sagiroglu

Authors
  1. Adem Kalinli
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  2. Seref Sagiroglu
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Editor information

Editors and Affiliations

  1. Dept. of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Adnan Yazıcı

  2. Department of Computer Engineering, Middle East Technical University, 06531, Ankara, Turkey

    Cevat Åžener

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© 2003 Springer-Verlag Berlin Heidelberg

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Kalinli, A., Sagiroglu, S. (2003). A New Approach Based on Recurrent Neural Networks for System Identification. In: Yazıcı, A., Şener, C. (eds) Computer and Information Sciences - ISCIS 2003. ISCIS 2003. Lecture Notes in Computer Science, vol 2869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39737-3_71

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  • DOI: https://doi.org/10.1007/978-3-540-39737-3_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20409-1

  • Online ISBN: 978-3-540-39737-3

  • eBook Packages: Springer Book Archive

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