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中文摘要：
给出求解广义线性互补问题的-个基于梯度的神经网络模型,分析了模型的平衡点与原问题解的关系,运用Lyapunov稳定性理论和LaSalle不变集原理,证明了该网络全局收敛于问题的解集,数值模拟表明网络不仅可行而且有效.
英文摘要：
We present a gradient-based neural network for solving a general linear complementarity problem and analyse the relationship between the equilibrium point of the network and the solution of the problem. With the Lyapunov theorem and LaSalle invariant set principle, the network is shown to be Lyapunov stable and globally converge to the solution set of the problem. Feasibility and efficiency of the network are further supported by an illustrative example.
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