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Input Dimension Determination of Linear Feedback Neural Network Applied for System Identification of Linear Systems

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

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Abstract

In the application of a linear neural network (LNN) to linear system identification and parameter estimation, it is important to determine the input dimension of the LNN so that the identification can be performed efficiently. In the LNN for linear system identification, both the input and output data are taken as input of the LNN. The output data are delayed and are fed-back to input of the LNN. The input dimension determination is to determine the right number of past inputs should be applied to its input and the right number of past outputs should be fed-back to its input also. The sampled input and output data are used to train the LNN. The performance errors are collected during training and are used in the evaluation by Akaike’s Information Criterion to determine the input dimension. The advantage of LNN method is its simplicity and effectiveness. Satisfactory results from simulation are provided to show the effectiveness of the proposed algorithm.

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Author information

Authors and Affiliations

  1. Department of Engineering Technology, University of Arkansas at Little Rock, Little Rock, AR, 72204, USA

    Wenle Zhang

Authors
  1. Wenle Zhang
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Correspondence to Wenle Zhang .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Zhang, W. (2017). Input Dimension Determination of Linear Feedback Neural Network Applied for System Identification of Linear Systems. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_47

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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