Letters: State estimation for discrete-time neural networks with time-varying delays

Authors:
Shaoshuai Mou;Huijun Gao;Wenyi Qiang;Zhongyang Fei
Affiliations:
Department of Control Science and Engineering, Harbin Institute of Technology, China;Department of Control Science and Engineering, Harbin Institute of Technology, China;Department of Control Science and Engineering, Harbin Institute of Technology, China;Department of Control Science and Engineering, Harbin Institute of Technology, China
Venue:
Neurocomputing
Year:
2008

Citing 10
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New results on robust exponential stability for discrete recurrent neural networks with time-varying delays

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State estimation for neural networks with mixed interval time-varying delays

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Less conservative results of state estimation for neural networks with time-varying delay

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Neural networks letter: Delay-distribution-dependent state estimation for discrete-time stochastic neural networks with random delay

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Robust state estimation for discrete-time stochastic neural networks with probabilistic measurement delays

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Guaranteed performance state estimation of static neural networks with time-varying delay

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State estimation of recurrent neural networks with time-varying delay: A novel delay partition approach

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Neural networks letter: Stochastic state estimation for neural networks with distributed delays and Markovian jump

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Nonlinear discrete time neural network observer

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New delay-dependent exponential stability for discrete-time recurrent neural networks with mixed time-delays

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Robust state estimation for discrete-time BAM neural networks with time-varying delay

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Abstract

In this paper, the problem of state estimation for discrete-time neural networks with time-varying delays is investigated. Attention is focused on the design of a state estimator to estimate the neuron states, through available output measurements. First, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the existence of admissible state estimators. These conditions are expressed in the form of LMIs, which guarantee the estimation error to be globally exponentially stable in the presence of time-varying delays. Then, the desired estimator matrix gain is characterized in terms of the solution to these LMIs. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design method.