Improved robust stability criteria for delayed cellular neural networks via the LMI approach
IEEE Transactions on Circuits and Systems II: Express Briefs
New results for robust stability of dynamical neural networks with discrete time delays
Expert Systems with Applications: An International Journal
International Journal of Systems Science
International Journal of Systems Science
Information Sciences: an International Journal
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The synchronization problem is studied in this paper for non-identical chaotic neural networks with time delays and fully unknown parameters, where the mismatched parameters, activation functions and neural network architectures are taken into account. To overcome the difficulty that complete synchronization of non-identical chaotic neural networks cannot be achieved only by utilizing output feedback control, we design an adaptive sliding mode controller to realize the synchronization. Our synchronization criteria are easily verified and do not need to solve any linear matrix inequality. These results generalize a few previous known results and remove some restrictions on the parameters, activation functions and neural network architectures. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.