DocumentCode
582103
Title
Convergence analysis of discrete-time simplified dual neural network for solving convex quadratic programming problems
Author
Yang, Lu ; Dewei, Li ; Yugeng, Xi ; Jianbo, Lu
Author_Institution
Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China
fYear
2012
fDate
25-27 July 2012
Firstpage
3305
Lastpage
3310
Abstract
The convergence property of discrete-time simplified dual neural network for convex quadratic programming is investigated. By choosing a proper Lyapunov function, a sufficient condition for global convergence is obtained. The convergence rate under the condition is also analyzed, and the exponential convergence property under the condition is proved. Simulation verifies the validity of the theoretical results obtained in this paper.
Keywords
Lyapunov methods; convex programming; discrete time systems; neural nets; Lyapunov function; convergence analysis; convergence property; discrete-time simplified dual neural network; solving convex quadratic programming problems; Automation; Convergence; Educational institutions; Electronic mail; Laboratories; Neural networks; Quadratic programming; Convergence; Discrete-Time; Neural Network; Quadratic Programming;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (CCC), 2012 31st Chinese
Conference_Location
Hefei
ISSN
1934-1768
Print_ISBN
978-1-4673-2581-3
Type
conf
Filename
6390492
Link To Document