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State estimation for discrete-time neural networks with time-varying delay

Author

Listed:
  • Zhengguang Wu
  • Peng Shi
  • Hongye Su
  • Jian Chu

Abstract

This article deals with the problem of delay-dependent state estimation for discrete-time neural networks with time-varying delay. Our objective is to design a state estimator for the neuron states through available output measurements such that the error state system is guaranteed to be globally exponentially stable. Based on the linear matrix inequality approach, a delay-dependent condition is developed for the existence of the desired state estimator via a novel Lyapunov functional. The obtained condition has less conservativeness than the existing ones, which is demonstrated by a numerical example.

Suggested Citation

  • Zhengguang Wu & Peng Shi & Hongye Su & Jian Chu, 2012. "State estimation for discrete-time neural networks with time-varying delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(4), pages 647-655.
    • Handle: RePEc:taf:tsysxx:v:43:y:2012:i:4:p:647-655
    DOI: 10.1080/00207721.2010.517870
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    Cited by:

    1. Ge, Chao & Wang, Bingfang & Wei, Xian & Liu, Yajuan, 2017. "Exponential synchronization of a class of neural networks with sampled-data control," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 150-161.
    2. Yongming Li & Shaocheng Tong, 2016. "Adaptive fuzzy switched control design for uncertain nonholonomic systems with input nonsmooth constraint," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(14), pages 3436-3446, October.
    3. S.G. Li & L. Shi, 2014. "The recommender system for virtual items in MMORPGs based on a novel collaborative filtering approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2100-2115, October.

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