scholarly journals Solving Pseudomonotone Variational Inequalities and Pseudoconvex Optimization Problems Using the Projection Neural Network

2006 ◽  
Vol 17 (6) ◽  
pp. 1487-1499 ◽  
Author(s):  
Xiaolin Hu ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Variational Inequalities ◽  
Optimization Problems ◽  
Projection Neural Network

A DELAYED NEURAL NETWORK METHOD FOR SOLVING CONVEX OPTIMIZATION PROBLEMS

2006 ◽  
Vol 16 (04) ◽  
pp. 295-303 ◽  
Author(s):  
YONGQING YANG ◽  
JINDE CAO
Keyword(s):  
Neural Network ◽  
Convex Programming ◽  
Global Exponential Stability ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Existence Of Solution ◽  
Convex Programming Problem ◽  
Convex Optimization Problems ◽  
Network Method ◽  
Projection Neural Network

In this paper, the delayed projection neural network for a class of solving convex programming problem is proposed. The existence of solution and global exponential stability of the proposed network are proved, which can guarantee to converge at an exact optimal solution of the convex programming problems. Several examples are given to show the effectiveness of the proposed network.

An Inertial Projection Neural Network for Solving Variational Inequalities

2017 ◽  
Vol 47 (3) ◽  
pp. 809-814 ◽  
Author(s):  
Xing He ◽  
Tingwen Huang ◽  
Junzhi Yu ◽  
Chuandong Li ◽  
Chaojie Li
Keyword(s):  
Neural Network ◽  
Variational Inequalities ◽  
Projection Neural Network

scholarly journals A Nonlinear Projection Neural Network for Solving Interval Quadratic Programming Problems and Its Stability Analysis

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Huaiqin Wu ◽  
Rui Shi ◽  
Leijie Qin ◽  
Feng Tao ◽  
Lijun He
Keyword(s):  
Neural Network ◽  
Global Exponential Stability ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Saddle Point Theorem ◽  
Set Constraints ◽  
Nonlinear Projection ◽  
Quadratic Programming Problems ◽  
Projection Neural Network ◽  
Interval Quadratic Programming

This paper presents a nonlinear projection neural network for solving interval quadratic programs subject to box-set constraints in engineering applications. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the interval quadratic optimization problems. By employing Lyapunov function approach, the global exponential stability of the proposed neural network is analyzed. Two illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.

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