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Improved delay-dependent stability condition of discrete recurrent neural networks with time-varying delays
Zhengguang Wu1, Hongye Su, Jian Chu
1Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, Zhejiang, China. nashwzhg@yahoo.com.cn
Abstract:
This brief investigates the problem of global exponential stability analysis for discrete recurrent neural networks with time-varying delays. In terms of linear matrix inequality (LMI) approach, a novel delay-dependent stability criterion is established for the considered recurrent neural networks via a new Lyapunov function. The obtained condition has less conservativeness and less number of variables than the existing ones. Numerical example is given to demonstrate the effectiveness of the proposed method.
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