Solving Generally Constrained Generalized Linear Variational Inequalities Using the General Projection Neural Networks

Abstract. The general projection neural network (GPNN) is a versatile recurrent neural network model capable of solving a variety of optimization problems and variational inequalities. In a recent article [IEEE Trans. Neural Netw., 18(6), 1697-1708, the linear case of GPNN was studied extensively from the viewpoint of stability analysis, and it was utilized to solve the generalized linear variational inequality with various types of constraints. In the present paper we supplement three global exponential convergence results for the GPNN for solving these problems. The first one is different from those shown in the original article, and the other two are improved versions of two results in that article. The validity of the new results are demonstrated by numerical examples.

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Solving Generally Constrained Generalized Linear Variational Inequalities Using the General Projection Neural Networks

Abstract: Abstract-Generalized linear variational inequality (GLVI) is an

Cited by 26 publications

References 21 publications

Real-time Motion Planning of Kinematically Redundant Manipulators Using Recurrent Neural Networks

Real-time Motion Planning of Kinematically Redundant Manipulators Using Recurrent Neural Networks

Solving the k-Winners-Take-All Problem and the Oligopoly Cournot-Nash Equilibrium Problem Using the General Projection Neural Networks

Three Global Exponential Convergence Results of the GPNN for Solving Generalized Linear Variational Inequalities

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