Improved results on state estimation for neural networks with time-varying delays |
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Authors: | Tao LI Shumin FEI HongLU |
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Affiliation: | 1. School of Instrument Science & Engineering,Southeast University,Nanjing Jiangsu 210096,China 2. Key Laboratory of Measurement and Control of Complex Systems of Engineering,Ministry of Education,Southeast University,Nanjing Jiangsu 210096,China |
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Abstract: | In this paper, some improved results on the state estimation problem for recurrent neural networks withboth time-varying and distributed time-varying delays are presented. Through available output measurements, an improveddelay-dependent criterion is established to estimate the neuron states such that the dynamics of the estimation error isglobally exponentially stable, and the derivative of time-delay being less than 1 is removed, which generalize the existentmethods. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed results. |
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Keywords: | Exponential state estimator Recurrent neural networks Exponential stability Time-varying delays Linearmatrix inequality (LMI) |
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