Existence and Global Exponential Stability of Equilibrium Solution to Reaction-Diffusion Recurrent Neural Networks on Time Scales. (1st August 2010)

• Record Type: Journal Article
• Authors: Zhao, Kaihong; Li, Yongkun
• Other Names: Solis Francisco Academic Editor.
• Is Part Of: Discrete dynamics in nature and society. Volume 2010(2010)
• Publication Date: 2010-08-01
• Subjects: System analysis -- Periodicals; Dynamics -- Periodicals; Chaotic behavior in systems -- Periodicals; Differentiable dynamical systems -- Periodicals; 003.05
• DOI: 10.1155/2010/624619
• Languages: English
• ISSNs: 1026-0226

Abstract

Abstract : 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.