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Exponential synchronization of a class of neural networks with time-varying delays

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4 Author(s)
Chao-Jung Cheng ; Dept. of Inf. Eng., Kun Shan Univ., Tainan, Taiwan ; Teh-Lu Liao ; Jun-Juh Yan ; Hwang, Chi-Chuan

This paper aims to present a synchronization scheme for a class of delayed neural networks, which covers the Hopfield neural networks and cellular neural networks with time-varying delays. A feedback control gain matrix is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory, and its exponential synchronization condition can be verified if a certain Hamiltonian matrix with no eigenvalues on the imaginary axis. This condition can avoid solving an algebraic Riccati equation. Both the cellular neural networks and Hopfield neural networks with time-varying delays are given as examples for illustration.

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:36 ,  Issue: 1 )

Date of Publication:

Feb. 2006

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