Original scientific paper
https://doi.org/10.1080/00051144.2018.1552651
Mean-square asymptotical synchronization control and robust analysis of discrete-time neural networks with time-varying delay
De-hui Lin
; State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, People’s Republic of China
Jun Wu
; State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, People’s Republic of China
Yang Zhu
; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
Jian-ping Cai
; Zhejiang University of Water Resources and Electric Power, Hangzhou, People’s Republic of China
Jian-ning Li
; School of Automation,Hangzhou Dianzi University, Hangzhou, People’s Republic of China
Abstract
This paper investigates the controller design problem for mean-square asymptotical synchronization of discrete-time neural networks with time-varying delay. We proposed the design method of synchronization controller, which considered the nonlinearity of controller input.
Based on the designed controller, a delay-dependent synchronization criterion is proposed and formulated in the form of linear matrix inequalities (LMIs) by applying the Lyapunov function method. The result is extended to the delayed discrete-time neural network with uncertainty. Two numerical examples are presented to illustrate the effectiveness of the proposed method.
Keywords
Asymptotical synchronization control; discrete-time neural networks; time-varying delay; controller nonlinearity; uncertainty
Hrčak ID:
225219
URI
Publication date:
12.12.2018.
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