The University of Southampton
University of Southampton Institutional Repository

Neurodynamical Optimization

Record type: Article

Dynamical (or ode) system and neural network approaches for optimization have been co-existed for two decades. The main feature of the two approaches is that a continuous path starting from the initial point can be generated and eventually the path will converge to the solution. This feature is quite different from conventional optimization methods where a sequence of points, or a discrete path, is generated.
Even dynamical system and neural network approaches share many common features and structures, yet a complete comparison for the two approaches has not been available. In this paper, based on a detailed study on the two approaches, a new approach, termed neurodynamical approach, is introduced.
The new neurodynamical approach combines the attractive features in both dynamical (or ode) system and neural network approaches. In addition, the new approach suggests a systematic procedure and framework on how to construct a neurodynamical system for both unconstrained and constrained problems. In analyzing the stability issues of the underlying dynamical (or ode) system, the neurodynamical approach adopts a new strategy, which avoids the Lyapunov function. Under the framework of this neurodynamical approach, strong theoretical results as well as promising numerical results are obtained.

Full text not available from this repository.

Citation

Liao, Li-Zhi, Qi, Houduo and Qi, Liqun (2004) Neurodynamical Optimization Journal of Global Optimization, 28, (2), pp. 175-195. (doi:10.1023/B:JOGO.0000015310.27011.02).

More information

Published date: 2004
Keywords: dynamical system, neural network, neurodynamical, ode system, optimization
Organisations: Operational Research

Identifiers

Local EPrints ID: 29649
URI: http://eprints.soton.ac.uk/id/eprint/29649
ISSN: 0925-5001
PURE UUID: b57698b4-7660-4d72-822c-4f401566a6b1

Catalogue record

Date deposited: 12 May 2006
Last modified: 17 Jul 2017 15:57

Export record

Altmetrics

Contributors

Author: Li-Zhi Liao
Author: Houduo Qi
Author: Liqun Qi

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×