BI-DIRECTIONAL COHEN-GROSSBERG NEURAL NETWORK WITH DISCRETE TIME

BI-DIRECTIONAL COHEN-GROSSBERG NEURAL NETWORK WITH DISCRETE TIME

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摘要 Discrete-time version of the bi-directional Cohen-Grossberg neural network is stud-ied in this paper. Some sufficient conditions are obtained to ensure the global exponen-tial stability of such networks with discrete time based on Lyapunov method. These results do not require the symmetry of the connection matrix and the monotonicity, boundedness and differentiability of the activation function. Discrete-time version of the bi-directional Cohen-Grossberg neural network is stud-ied in this paper. Some sufficient conditions are obtained to ensure the global exponen-tial stability of such networks with discrete time based on Lyapunov method. These results do not require the symmetry of the connection matrix and the monotonicity, boundedness and differentiability of the activation function.

作者 Du Dejun1, Chen Anping1,2 (1. Dept. of Math., Xiangtan University, Xiangtan 411005, Hunan 2. Dept. of Math., Xiangnan University, Chenzhou 423000, Hunan)

出处《Annals of Differential Equations》 2009年第1期20-31,共12页 微分方程年刊（英文版）

基金 supported by Scientific Research Foundation of Hunan Provincial EducationDepartment (04A055, 05A057,03C009)

关键词 bi-directional Cohen-Grossberg neural network DISCRETE-TIME global exponential stability Lyapunov function bi-directional Cohen-Grossberg neural network discrete-time global exponential stability Lyapunov function

分类号 O175 [理学—基础数学]

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参考文献10

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Annals of Differential Equations

2009年第1期

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BI-DIRECTIONAL COHEN-GROSSBERG NEURAL NETWORK WITH DISCRETE TIME

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