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Passivity Analysis of a General Form of Recurrent Neural Network with Multiple Delays

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

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Abstract

In this paper, by using some analytic techniques, several sufficient conditions are given to ensure the passivity of a general form of recurrent neural network with multiple delays. The passivity conditions are presented in terms of a negative semi-definite matrix declared. They are easily verifiable and easier to check computing with some conditions in terms of complicated linear matrix inequality.

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Author information

Authors and Affiliations

  1. Department of Electric and Electronic Engineering, Wuhan Institute of Shipbuilding Technology, Wuhan, Hubei, 430050, China

    Jinhua Huang & Jiqing Liu

Authors
  1. Jinhua Huang
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  2. Jiqing Liu
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, SD 57701, Rapid City, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Huang, J., Liu, J. (2009). Passivity Analysis of a General Form of Recurrent Neural Network with Multiple Delays. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_86

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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