矩阵型投影神经网络模型解混合约束非线性优化

A Matrix Projection Neural Network Model for Solving Mixed Constrained Nonlinear Optimization

构建神经网络模型,并证明该模型的稳定性是求解非线性优化的重要问题,矩阵变量神经网络模型是向量型神经网络的拓展,大量研究工作者证明了前者在计算速度与应用方面都更有优势。研究针对一类含有混合约束的非线性规划,提出了一种新的矩阵型投影神经网络,并证明了模型的全局稳定性,模拟实验进一步验证了此结论。

A neural network model is constructed,and the stability of the model is proved to be an important problem in solving nonlinear optimization.Matrix variable neural network model is an extension of vector neural network.A large number of researchers have proved that the former has more advantages in computational speed and application.A new matrix projection neural network is proposed for a class of nonlinear programming with mixed constraints,and the global stability of the model is proved.The simulation experiments further verify the conclusion.