摘要
研究了线性互补问题.基于解的充分必要条件,提出了求解它的一个神经网络模型;构造了恰当的Liapunov函数,给出了该模型稳定和大范围渐近收敛的充分条件;研究了其全局指数稳定性,并用数值实例说明了该模型的可行性和有效性.该模型不需要设定网络参数,可用来求解一类非单调的互补问题.
A neural network for solving the linear complementarity problem by using the necessary and sufficient conditions of its solution is proposed in this paper. A sufficient condition for its stability and asymptotic convergence to be confirmed is given by defining its Liapunov function. Furthermore, its global exponential stability is also discussed. The feasibility and effectiveness of the proposed neural network are shown by numerical examples. No parameter is involved, and it can be used to solve a class of nonmonotonic complementarity problems.
出处
《陕西师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2003年第1期25-29,共5页
Journal of Shaanxi Normal University：Natural Science Edition
