Existence and Global Exponential Stability of Equilibrium Solution to Reaction-Diffusion Recurrent Neural Networks on Time Scales

被引：5
|
作者：
Zhao, Kaihong [1 ,2 ]
Li, Yongkun [1 ]
机构：
[1] Yunnan Univ, Dept Math, Kunming 650091, Yunnan, Peoples R China
[2] Kunming Univ Sci & Technol, Dept Appl Math, Kunming 650093, Yunnan, Peoples R China
来源：
DISCRETE DYNAMICS IN NATURE AND SOCIETY | 2010年 / 2010卷
关键词：
DISTRIBUTED DELAYS; PERIODIC-SOLUTIONS; ROBUST STABILITY; CONVERGENCE; SYSTEMS; LOTKA;
D O I：
10.1155/2010/624619
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The existence of equilibrium solutions to reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales is proved by the topological degree theory and M-matrix method. Under some sufficient conditions, we obtain the uniqueness and global exponential stability of equilibrium solution to reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales by constructing suitable Lyapunov functional and inequality skills. One example is given to illustrate the effectiveness of our results.
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页数：12
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