一种求解半定线性互补问题的神经网络方法

A Neural Network Approach for Solving Semi-Definite Linear Complementarity Problem

摘要: 本文提出了一种新颖的投影神经网络方法，用于求解半定线性互补问题(SDLCP)。理论分析表明，在特定条件下，所提方法能够保证指数稳定性。并且通过MATLAB仿真实验给出了数值算例，充分证明了所提投影神经网络模型的有效性。本文提出的神经网络框架为求解半定线性互补问题提供了一种具有前景且切实可行的方法，具有广阔的应用潜力。

Abstract: This paper proposes a novel projection neural network method for solving the Semi-Definite Linear Complementarity Problem (SDLCP). Theoretical analysis shows that under specific conditions, the proposed method can guarantee exponential stability. Moreover, an example is presented through MATLAB simulation experiments, which fully demonstrates the effectiveness of the proposed projection neural network model. The neural network framework proposed in this paper provides a promising and practical approach for solving the Semi-Definite Linear Complementarity Problem, with broad application potential.