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		Tao LI,Shumin FEI,HongLU.[en_title][J].Control Theory and Technology,2010,8(2):215~221.[Copy]

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TaoLI,ShuminFEI,HongLU
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DOI：10.1007/s11768-010-7157-8

Received:July 19, 2007Revised:October 24, 2008

基金项目:The National Natural Science Foundation of China (No.60764001, 60835001, 60875035)

Improved results on state estimation for neural networks with time-varying delays

Tao LI,Shumin FEI,HongLU

(School of Instrument Science & Engineering, Southeast University;Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education,Southeast University)

Abstract:

In this paper, some improved results on the state estimation problem for recurrent neural networks with both time-varying and distributed time-varying delays are presented. Through available output measurements, an improved delay-dependent criterion is established to estimate the neuron states such that the dynamics of the estimation error is globally exponentially stable, and the derivative of time-delay being less than 1 is removed, which generalize the existent methods. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed results.

Key words: Exponential state estimator Recurrent neural networks Exponential stability Time-varying delays Linearmatrix inequality (LMI)