Neural Network Approach for Semivectorial Bilevel Programming Problem

Author

Lv, Yibing

Author_Institution

Sch. of Inf. & Math., Yangtze Univ., Jingzhou, China

Volume

2

fYear

2012

fDate

26-27 Aug. 2012

Firstpage

30

Lastpage

33

Abstract

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.

Keywords

Lyapunov methods; asymptotic stability; backpropagation; linear programming; mathematics computing; neural nets; vectors; Lyapunov stability; asymptotic stability; linear multiobjective programming; neural network; scalar-valued optimization problem; semivectorial BP problem; semivectorial bilevel programming problem; Asymptotic stability; Neural networks; Optimization; Programming; Smoothing methods; Transient analysis; Vectors; asymptotic stability; neural network; optimal solution; semivectorial bilevel programming problem;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on

Conference_Location

Nanchang, Jiangxi

Print_ISBN

978-1-4673-1902-7

Type

conf

DOI

10.1109/IHMSC.2012.103

Filename

6305717