Delay-dependent passivity criterion for discrete-time delayed standard neural network model

Authors:
Jin Zhu;Qingling Zhang;Zhonghu Yuan
Affiliations:
Institute of Systems Science, Northeastern University, Shenyang, Liaoning 110004, PR China and Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern Univ ...;Institute of Systems Science, Northeastern University, Shenyang, Liaoning 110004, PR China and Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern Univ ...;School of Information Science and Engineering, Shenyang University, Shenyang, Liaoning 110044, PR China
Venue:
Neurocomputing
Year:
2010

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Abstract

In this paper, the problem of robust passivity for discrete-time delayed standard neural network model (DDSNNM) with time-varying delays and norm-bounded parameters uncertainties is investigated. The model is the interconnection of a linear dynamic system and a bounded static delayed nonlinear operator. The DDSNNM is applied to analyze the passivity of discrete-time recurrent neural networks and synthesize the state-feedback passive controller for discrete-time nonlinear system modeled by the neural networks. By constructing suitable Lyapunov-Krasovskii functional, the delay-dependent passivity criterion for discrete-time delayed standard neural network model is obtained in terms of linear matrix inequality. Numerical examples are given to illustrate the effectiveness of the proposed methods.