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State Estimation of Markovian Jump Neural Networks with Mixed Time Delays

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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

This paper is concerned with the state estimation problem of Markovian jump neural networks with discrete and distributed delays. A stochastic Lyapunov functional with a triple-integral term is constructed to handle it. A delay-dependent design criterion is derived such that the resulting error system is mean square exponentially stable with a prescribed decay rate. The gain matrices of the state estimator and the decay rate can be obtained by solving some coupled linear matrix inequalities.

This work was jointly supported by the National Natural Science Foundation of China under Grant No. 61005047 and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK2010214.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, P.R. China

    He Huang & Xiaoping Chen

Authors
  1. He Huang
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  2. Xiaoping Chen
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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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Huang, H., Chen, X. (2012). State Estimation of Markovian Jump Neural Networks with Mixed Time Delays. 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_16

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  • DOI: https://doi.org/10.1007/978-3-642-31346-2_16

  • 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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