Robust control of a class of neural networks with bounded uncertainties and time-varying delays

Authors:
Chao-Jung Cheng
Affiliations:
Department of Information Engineering, Kun Shan University, Tainan 710, Taiwan, ROC
Venue:
Computers & Mathematics with Applications
Year:
2008

Citing 6
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Robust adaptive control of a class of nonlinear systems with unknown dead-zone

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Letters: Robust decentralized adaptive control for stochastic delayed Hopfield neural networks

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Abstract

This paper investigates the robust control problem for a class of neural networks subject to bounded uncertainties and time-varying delays. A memoryless decentralized variable structure control law with dead-zone input for guaranteeing global asymptotical system stability is derived. The results demonstrate that the derived control law does not restrict the derivative of the time-varying delays even if dead-zone nonlinearity occurs in the control input. Such a control law can be used to stabilize Cohen-Grossberg neural networks, cellular neural networks and Hopfield neural networks; all of which have bounded uncertainties and time-varying delays. Two examples are provided to illustrate the effectiveness and validity of the proposed control scheme.