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Absolute exponential stability of a class of continuous-time recurrent neural networks

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2 Author(s)
Sanqing Hu ; Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, China ; Jun Wang

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.

Published in:

IEEE Transactions on Neural Networks  (Volume:14 ,  Issue: 1 )