A delayed projection neural network for solving linear variational inequalities.
In this paper, a delayed projection neural network is proposed for solving a class of linear variational inequality problems. The theoretical analysis shows that the proposed neural network is globally exponentially stable under different conditions. By the proposed linear matrix inequality (LMI) method, the monotonicity assumption on the linear variational inequality is no longer necessary. By employing Lagrange multipliers, the proposed method can resolve the constrained quadratic programming problems. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed neural network.
Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. chenglong@compsys.ia.ac.cn
Long
L
Cheng
Zeng-Guang
ZG
Hou
Min
M
Tan
D000465:Algorithms; D003198:Computer Simulation; D016014:Linear Models; D016571:Neural Networks, Computer