DocumentCode :
458816
Title :
Application of the Transiently Chaotic Neural Network to Nonlinear Constraint Optimization Problems
Author :
Li, Xinyu ; Chen, Dongyi
Author_Institution :
Sch. of Autom. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu
Volume :
1
fYear :
2006
fDate :
16-18 Oct. 2006
Firstpage :
90
Lastpage :
94
Abstract :
To deal with the deficiencies of the neural network model based on Hopfield neural network (HNN) for nonlinear constraint optimization problems that is easily trapped in local minimum, a novel optimization network model based on transiently chaotic network (TCNN) is proposed in this paper. Because TCNN has richer and more flexible dynamics compared to HNN, this network model that combined with Lagrange multiplier theory has higher ability of searching for globally optimal solutions to the nonlinear constraint optimization problems. Its asymptotic stability is proved and its equilibrium point is the optimal point of the original problem. The simulation results illustrate the effectiveness of this optimal network algorithm
Keywords :
Hopfield neural nets; asymptotic stability; chaos; mathematics computing; optimisation; Hopfield neural network model; Lagrange multiplier theory; asymptotic stability; local minimum; nonlinear constraint optimization problem; optimal network algorithm; optimization network model; transiently chaotic neural network; Asymptotic stability; Automation; Chaos; Constraint optimization; Electron traps; Hopfield neural networks; Lagrangian functions; Neural networks; Neurons; Parallel programming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location :
Jinan
Print_ISBN :
0-7695-2528-8
Type :
conf
DOI :
10.1109/ISDA.2006.105
Filename :
4021415
Link To Document :
بازگشت