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An Improved Algebraic Criterion for Global Exponential Stability of Recurrent Neural Networks With Time-Varying Delays

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2 Author(s)
Shen, Yi ; Huazhong Univ. of Sci. & Technol., Wuhan ; Jun Wang

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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Neural Networks, IEEE Transactions on  (Volume:19 ,  Issue: 3 )