求解混合约束非线性规划的神经网络模型

Neural Network for Nonlinear Programming Problems with Hybrid Constraints

通过巧妙构造Liapunov函数,提出一种大范围收敛的求解优化问题的连续神经网络模型.它具有良好的功能和性能,可以求解具有等式和不等式约束的非线性规划问题.该模型是Newton最速下降法对约束问题的推广,能有效地提高解的精度.即使对正定二次规划问题,它也比现有的模型结构简单.

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.