Neural Network for Mixed Nonlinear Problems and its Applications
This paper presents two feedback neural networks for solving a nonlinear and mixed complementarity problem. The first feedback neural network is designed to solve the strictly monotone problem. This one .ias no parameter and possesses a very simple structure for implementation in hardware. Based on a new idea,the second feedback neural network for solving the monotone problem is constructed by using the first one as a subnetwork. This feedback neural network has the least number of state variables. The stability of a solution of the problem is proved. When the problem is strictly monotone,the unique solution is uniformly and asymptotically stable in the large. When the problem has many solutions,it is guaranteed that,for any initial point,the trajectory of the network does converge to an exact solution of the problem. Feasibility and efficiency of the proposed neural networks are supported by simulation experiments. Moreover,the feedback neural network can also be applied to solve general nonlinear convex programming and nonlinear monotone variational inequalities problems with convex constraints.
Feedback neural network Asymptotic stability Variational inequalities Nonlinear programming
Jifu Nong
College of Mathematics and Computer Science,Guangxi University for Nationalities,Nanning,China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis,Nanning,China
国际会议
西安
英文
1771-1775
2011-12-23（万方平台首次上网日期，不代表论文的发表时间）