Cart (Loading....) | Create Account
Close category search window

Hybrid Coupled Local Minimizers

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.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xuyang Lou ; Key Lab. of Adv. Process Control for Light Ind. (Minist. of Educ.), Jiangnan Univ., Wuxi, China ; Suykens, J.A.K.

This paper proposes an improved global optimization technique, named Hybrid Coupled Local Minimizers (HCLM), which is inspired on the method of coupled local minimizers (CLM). The HCLM method uses a set of search points, called “particles”, initially spread over the search space, that are occasionally impulsively coupled, instead of permanently coupled. This approach leads to a much better optimization efficiency, because it combines the fast convergence (due to a Newton-based method that is used for each particle) with the capability of global optimization (resulting from the hybrid interaction which enables a parallel strategy). Such parallel and distributed computing process embedded with a trust region strategy relies on the hybrid interconnections of particles rather than individual particles, which is able to fully exploit the parallel nature of the computation and produce results which are more globally optimal. It is worth mentioning that the stability of each particle and the synchronization of all particles are also derived and proved by means of the contraction theory. The HCLM method is illustrated on several test functions with many local minima and applied to a problem of static nonlinear regression with multilayer perceptrons (MLPs) from given noisy measurement data.

Published in:

Circuits and Systems I: Regular Papers, IEEE Transactions on  (Volume:61 ,  Issue: 2 )

Date of Publication:

Feb. 2014

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.