A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems

2016 ◽  
Vol 27 (2) ◽  
pp. 214-224 ◽  
Author(s):  
Youshen Xia ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Optimization Problems ◽  
Quadratic Optimization ◽  
Constrained Quadratic Optimization ◽  
Projection Neural Network

scholarly journals Robust Linear Neural Network for Constrained Quadratic Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Zixin Liu ◽  
Yuanan Liu ◽  
Lianglin Xiong
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Perturbation Theory ◽  
Optimization Problems ◽  
Quadratic Optimization ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Analysis Method ◽  
Linear Neural Network ◽  
Constrained Quadratic Optimization

Based on the feature of projection operator under box constraint, by using convex analysis method, this paper proposed three robust linear systems to solve a class of quadratic optimization problems. Utilizing linear matrix inequality (LMI) technique, eigenvalue perturbation theory, Lyapunov-Razumikhin method, and LaSalle’s invariance principle, some stable criteria for the related models are also established. Compared with previous criteria derived in the literature cited herein, the stable criteria established in this paper are less conservative and more practicable. Finally, a numerical simulation example and an application example in compressed sensing problem are also given to illustrate the validity of the criteria established in this paper.

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.

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