计算机工程与应用2019，Vol.55Issue(12)：37-43,7.DOI:10.3778/j.issn.1002-8331.1902-0116
一种解决非光滑伪凸优化问题的新型神经网络
New Neural Network for Solving Nonsmooth Pseudoconvex Optimization Problems
摘要
Abstract
Aiming at the nonsmooth pseudoconvex optimization problem with inequality constraints, a new recurrent neu-ral network based on differential inclusion theory is proposed. According to the objective function and constraints, a penalty function is designed, which changes with the change of the state vector, so that the state vector of the neural network always moves in the direction of the feasible region and ensures that the state vector of the neural network can be in finite time. It enters the feasible region and converges to the optimal solution of the original optimization problem. Finally, two simula-tion experiments are used to verify the validity and accuracy of the neural network. Compared with the existing neural net-work, it is a new type of neural network model. The model has simple structure. It does not need to calculate the exact pen-alty factor, and most importantly, and it also does not need the bounded feasible region.关键词
非光滑伪凸函数/神经网络/收敛/优化问题Key words
nonsmooth pseudoconvex functions/ neural networks/ convergence/ optimization problems分类
信息技术与安全科学引用本文复制引用
喻昕,伍灵贞,汪炎林..一种解决非光滑伪凸优化问题的新型神经网络[J].计算机工程与应用,2019,55(12):37-43,7.基金项目
国家自然科学基金(No.61862004,No.61462006). （No.61862004,No.61462006）
