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An improved algebraic criterion for global exponential stability of recurrent neural networks with time-varying
1Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China. yishen64@163.com
Abstract:
This brief paper presents an M-matrix-based algebraic criterion for the global exponential stability of a class of recurrent neural networks with decreasing time-varying delays. The criterion improves some previous criteria based on M-matrix and is easy to be verified with the connection weights of the recurrent neural networks with decreasing time-varying delays. In addition, the rate of exponential convergence can be estimated via a simple computation based on the criterion herein.
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