Robust exponential stability analysis for unertain neural networks with time delay
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Absolute stability of hopfield neural network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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This paper presents a new result on absolute exponential stability (AEST) of a class of continuous-time recurrent neural networks with locally Lipschitz continuous and monotone nondecreasing activation functions. The additively diagonally stable connection weight matrices are proven to be able to guarantee AEST of the neural networks. The AEST result extends and improves the existing absolute stability and AEST ones in the literature.