Chaos control and synchronization, with input saturation, via recurrent neural networks
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Computers & Mathematics with Applications
International Journal of Computer Mathematics - COMPLEX NETWORKS
Exponential synchronization of a class of neural networks with time-varying delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Synchronization and stable phase-locking in a network of neurons with memory
Mathematical and Computer Modelling: An International Journal
WSEAS Transactions on Mathematics
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The global exponential synchronization for a class of delayed reaction-diffusion cellular neural networks with Dirichlet boundary conditions is discussed. Some new sufficient conditions which include the diffusion coefficients are obtained by using the Lyapunov functional method, many real parameters and inequality techniques. Particularly, different from previous works, see references [S. Li, H. Yang, X. Lou, Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms, Chaos, Solitons Fractals 40 (2009) 930-939; X. Lou, B. Cui, Asymptotic synchronization of a class of neural networks with reaction-diffusion terms and time-varying delayes, Comput. Math. Appl. 52 (2006) 897-904; Y. Wang, J. Cao, Synchronization of a class of delayed neural networks with reaction-diffusion terms, Phys. Lett. A 369 (2007) 201-211], in our results the effect of the diffusion terms on the synchronization are considered for the first time. Finally, a numerical example is given to verify our results.