Input-to-state learning of recurrent neural networks with delay and disturbance |
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Authors: | Zhi Zhang Xin Huang Yebin Chen Jianping Zhou |
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Affiliation: | 1. School of Computer Science and Technology, Anhui University of Technology, Ma'anshan, China;2. School of Computer Science and Technology, Anhui University of Technology, Ma'anshan, China
Research Institute of Information Technology, Anhui University of Technology, Ma'anshan, China |
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Abstract: | This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given. |
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Keywords: | -gain delay input-to-state stability recurrent neural networks weight learning |
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