期刊
NEURAL NETWORKS
卷 18, 期 2, 页码 171-178出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2004.10.005
关键词
recurrent neural networks; global output convergence; time-varying input; Lyapunov diagonal stability; Lipschitz continuity; optimization
This paper studies the global output convergence of a class of recurrent neural networks with globally Lipschitz continuous and monotone nondecreasing activation functions and locally Lipschitz continuous time-varying inputs. We establish two sufficient conditions for global output convergence of this class of neural networks. Symmetry in the connection weight matrix is not required in the present results which extend the existing ones. (c) 2004 Elsevier Ltd. All rights reserved.
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