Neural Network for Nonlinear Programming Problems with Hybrid Constraints
求解混合约束非线性规划的神经网络模型

In this paper, a kind of globally convergent continuous neural network for optimization problems is presented by designing Liapunov function skillfully, it has better function and higher performance. It is capable of solving nonlinear programming problems with the constraints of equality and inequality. The proposed neural network is an extension of Newton deepest decedent method for constraint problems, it can improve the accuracy of the solutions, and its structure is simpler than the existing networks even when it is for solving positive definite quadratic programming problems.