Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
Global stability analysis of a class of delayed cellular neural networks
Mathematics and Computers in Simulation
Dynamics of general neural networks with distributed delays
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Delay-Dependent Exponential Stability of Cellular Neural Networks with Multi-Proportional Delays
Neural Processing Letters
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For the first time the global dissipativity of a class of cellular neural networks with multipantograph delays is studied. On the one hand, some delay-dependent sufficient conditions are obtained by directly constructing suitable Lyapunov functionals; on the other hand, firstly the transformation transforms the cellular neural networks with multipantograph delays into the cellular neural networks with constant delays and variable coefficients, and then constructing Lyapunov functionals, some delay-independent sufficient conditions are given. These new sufficient conditions can ensure global dissipativity together with their sets of attraction and can be applied to design global dissipative cellular neural networks with multipantograph delays and easily checked in practice by simple algebraic methods. An example is given to illustrate the correctness of the results.