期刊
NEURAL NETWORKS
卷 79, 期 -, 页码 117-127出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2016.03.010
关键词
Cohen-Grossberg neural networks; Multistability; Non-monotonic activation functions; Time-varying delays
资金
- Science and Technology Support Program of Hubei Province [2015BHE013]
- Program for Science and Technology in Wuhan of China [2014010101010004]
- Program for Changjiang Scholars and Innovative Research Team in University of China [IRT1245]
This paper addresses the multistability for a general class of recurrent neural networks with time-varying delays. Without assuming the linearity or monotonicity of the activation functions, several new sufficient conditions are obtained to ensure the existence of (2K + 1)(n) equilibrium points and the exponential stability of (K + 1)(n) equilibrium points among them for n-neuron neural networks, where K is a positive integer and determined by the type of activation functions and the parameters of neural network jointly. The obtained results generalize and improve the earlier publications. Furthermore, the attraction basins of these exponentially stable equilibrium points are estimated. It is revealed that the attraction basins of these exponentially stable equilibrium points can be larger than their originally partitioned subsets. Finally, three illustrative numerical examples show the effectiveness of theoretical results. (C) 2016 Elsevier Ltd. All rights reserved.
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