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Adaptive Stochastic Robust Convergence of Neutral-Type Neural Networks with Markovian Jump Parameters

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Advances in Neural Networks – ISNN 2012 (ISNN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7367))

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Abstract

The adaptive stochastic robust convergence and stability in mean square are investigated for a class of uncertain neutral-type neural networks with both Markovian jump parameters and mixed delays. The mixed delays consists of discrete and distributed time-varying delays. First, by employing the Lyapunov method and a generalized Halanay-type inequality, a delay-independent condition is derived to guarantee the state variables of the discussed neural networks to be globally uniformly exponentially stochastic convergent to a ball in the state space with a pre-specified convergence rate. Next, by applying the Jensen integral inequality and a novel Lemma, a delay-dependent criterion is developed to achieve the globally stochastic robust stability in mean square. The proposed conditions are all in terms of linear matrix inequalities, which can be solved numerically by employing the LMI toolbox in Matlab.

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Author information

Authors and Affiliations

  1. School of Science, Dalian Jiaotong University, Dalian, 116028, P.R. China

    Cheng-De Zheng & Chao-Ke Gong

  2. School of Information Science and Engineering, Northeastern University, Shenyang, 110004, P.R. China

    Zhanshan Wang

Authors
  1. Cheng-De Zheng
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  2. Chao-Ke Gong
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  3. Zhanshan Wang
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Editor information

Editors and Affiliations

  1. Department of Mechanical & Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Jun Wang

  2. School of Electrical and Computer Engineering, Oklahoma State University, 74078, Stillwater, OK, USA

    Gary G. Yen

  3. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios M. Polycarpou

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© 2012 Springer-Verlag Berlin Heidelberg

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Zheng, CD., Gong, CK., Wang, Z. (2012). Adaptive Stochastic Robust Convergence of Neutral-Type Neural Networks with Markovian Jump Parameters. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_40

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  • DOI: https://doi.org/10.1007/978-3-642-31346-2_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31345-5

  • Online ISBN: 978-3-642-31346-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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