Hamiltonian Matrix Strategy for Exponential Synchronization of Neural Networks With Diffusion
In this paper, the problem of exponential synchronization for a class of chaotic neural networks which covers the Hopfield neural networks and cellular neural networks with reaction-diffusion terms and time-varying delays is investigated. A feedback control gain matrix is derived to achieve the state synchronization of two identical neural networks with reaction-diffusion terms by using the Lyapunov stability theory, and the synchronization condition can be verified if a certain Hamiltonian matrix with no eigenvalue on the imaginary axis. This condition can avoid solving an algebraic Riccati equation. The results make a preparation for the research about synchronization of delayed neural networks and further the earlier researches. A numerical example illustrates the effectiveness of the results.
Exponential synchronization Chaotic neural networks Reaction-diffusion terms Time-varying Lyapunov functional
Xuyang Lou Baotong Cui
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan Univ
国内会议
厦门
英文
1-6
2012-08-01（万方平台首次上网日期，不代表论文的发表时间）