On the global output convergence of a class of recurrent neural networks with time-varying inputs

Authors:
Sanqing Hu;Derong Liu
Affiliations:
Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA;Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
Venue:
Neural Networks
Year:
2005

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Abstract

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.