A projection neural network with mixed delays for solving linear variational inequality

Authors:
Bonan Huang;Guotao Hui;Dawei Gong;Zhanshan Wang;Xiangping Meng
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, PR China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, PR China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, PR China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, PR China;School of Electrical and Information Engineering, Changchun Institute of Technology, Changchun, Jilin 130012, PR China
Venue:
Neurocomputing
Year:
2014

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Abstract

This paper presents a projection neural network with discrete delays and distributed delays (i.e. mixed delays) for solving linear variational inequality (LVI). By the Lyapunov theory and the linear matrix inequality (LMI) approach, the neural network is proved to be globally exponentially convergent to the solution of LVI. Compared with existing neural networks for solving LVI, the proposed one features the ability of solving a class of non-monotone LVI. One numerical example is provided to illustrate the effectiveness and the satisfactory performance of the neural network.