Neural network approach for semivectorial bilevel programming problem

被引：1
|
作者：
Lv, Yibing [1 ]
机构：
[1] Yangtze Univ, Sch Informat & Math, Jinzhou, Peoples R China
来源：
2012 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL 2 | 2012年
关键词：
semivectorial bilevel programming problem; neural network; asymptotic stability; optimal solution; PENALTY;
D O I：
10.1109/IHMSC.2012.103
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel neural network approach is proposed for solving semivectorial bilevel programming problem, where the upper level is a scalar-valued optimization problem and the lower level is the linear multiobjective programming. The proposed neural network is proved to be Lyapunov stable and capable of generating optimal solution to the semivectorial BP problem. The numerical result shows that the neural network approach is feasible and efficient.
引用
收藏
页码：30 / 33
页数：4
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