A recurrent neural network based on projection operator for extended general variational inequalities

Authors:
Qingshan Liu;Jinde Cao
Affiliations:
School of Automation, Southeast University, Nanjing, China;Department of Mathematics, Southeast University, Nanjing, China
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Year:
2010

Citing 22
Cited 6

Finite-dimensional variational inequality and nonlinear complementarity problems: a survey of theory, algorithms and applications

Mathematical Programming: Series A and B
Equivalent differentiable optimization problems and descent methods for asymmetric variational inequality problems

Mathematical Programming: Series A and B
Iterative solution of nonlinear equations in several variables

Iterative solution of nonlinear equations in several variables
On the stability of globally projected dynamical systems

Journal of Optimization Theory and Applications
Neural Networks for Optimization and Signal Processing

Neural Networks for Optimization and Signal Processing
A one-layer recurrent neural network with a discontinuous activation function for linear programming

Neural Computation
A new projection-based neural network for constrained variational inequalities

IEEE Transactions on Neural Networks
A dual neural network for kinematic control of redundant robotmanipulators

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A one-layer recurrent neural network for support vector machine learning

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A primal-dual neural network for online resolving constrained kinematic redundancy in robot motion control

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Recurrent Neural Network for Solving a Class of General Variational Inequalities

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design of General Projection Neural Networks for Solving Monotone Linear Variational Inequalities and Linear and Quadratic Optimization Problems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints

IEEE Transactions on Neural Networks
Global exponential stability of neural networks with globally Lipschitz continuous activations and its application to linear variational inequality problem

IEEE Transactions on Neural Networks
Exponential stability of globally projected dynamic systems

IEEE Transactions on Neural Networks
A general projection neural network for solving monotone variational inequalities and related optimization problems

IEEE Transactions on Neural Networks
A delayed neural network for solving linear projection equations and its analysis

IEEE Transactions on Neural Networks
A novel neural network for variational inequalities with linear and nonlinear constraints

IEEE Transactions on Neural Networks
Solving Pseudomonotone Variational Inequalities and Pseudoconvex Optimization Problems Using the Projection Neural Network

IEEE Transactions on Neural Networks
Solving Quadratic Programming Problems by Delayed Projection Neural Network

IEEE Transactions on Neural Networks
A One-Layer Recurrent Neural Network With a Discontinuous Hard-Limiting Activation Function for Quadratic Programming

IEEE Transactions on Neural Networks
An Improved Dual Neural Network for Solving a Class of Quadratic Programming Problems and Its -Winners-Take-All Application

IEEE Transactions on Neural Networks

Iterative methods for solving extended general mixed variational inequalities

Computers & Mathematics with Applications
A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization

Neural Networks
Efficient neural networks for solving variational inequalities

Neurocomputing
Letters: A compact neural network for training support vector machines

Neurocomputing
Batch gradient method with smoothing L1/ 2 regularization for training of feedforward neural networks

Neural Networks
Neural network for nonsmooth pseudoconvex optimization with general constraints

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the projection operator, a recurrent neural network is proposed for solving extended general variational inequalities (EGVIs). Sufficient conditions are provided to ensure the global convergence of the proposed neural network based on Lyapunov methods. Compared with the existing neural networks for variational inequalities, the proposed neural network is a modified version of the general projection neural network existing in the literature and capable of solving the EGVI problems. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed neural network.