The generalized dahlquist constant with applications in synchronization analysis of typical neural networks via general intermittent control

Authors:
Zhang Qunli
Affiliations:
Department of Mathematics, Heze University, Heze, Shandong, China
Venue:
Advances in Artificial Neural Systems
Year:
2011

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Abstract

A novel and effective approach to synchronization analysis of neural networks is investigated by using the nonlinear operator named the generalized Dahlquist constant and the general intermittent control. The proposed approach offers a design procedure for synchronization of a large class of neural networks. The numerical simulations whose theoretical results are applied to typical neural networks with and without delayed item demonstrate the effectiveness and feasibility of the proposed technique.