By Topic

An improved neural networks with transient chaos method for job-shop scheduling problems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
Xu Xin-li ; Zhejiang Univ. of Technol., Hangzhou, China ; Wang Wan-Liang

Having considered all the constraints of the job-shop scheduling problem (JSP), we present a new computational energy function of Hopfield neural networks for JSP. By introducing transient chaos and time-variant gain, an improved method to solve JSP by a neural network model with transient chaos is proposed, which can avoid Hopfield neural networks being sucked into local minima. The simulation results show that the modified method not only has the ability of searching for the global minimum, but can also converge to minimum quickly. More importantly, it can keep the steady output of neural networks as a feasible solution for JSP.

Published in:

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on  (Volume:3 )

Date of Conference: