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Constrained LMS for Dynamic Flow Networks

eftaxias, K, cheong took, C, venturini, B and arscott, D (2017) Constrained LMS for Dynamic Flow Networks In: International joint conference on neural networks, 2017-05-14 - 2017-05-19, Anchorage, Alaska USA.

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Abstract

In this era of climate change, there is a growing need to offer adaptive learning algorithms in the optimisation of natural resources. These resources are typically optimised by evolutionary algorithms. However, evolutionary algorithms (EAs) are no longer adequate due to the ‘drift’ component introduced by environmental factors such as flash flooding. We therefore propose a novel constrained Least Mean Squares (LMS) algorithm for the optimisation of flow networks. For rigor, we provide a stability analysis of our adaptive algorithm, which enables us to interpret the physical meaning of the network at equilibrium. We evaluate our proposed method against genetic algorithm (GA), the most common evolutionary algorithm. The results are promising: not only the proposed constrained LMS has a performance advantage over GA, but its computational cost is significantly lower making it more suitable for real-time applications.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computing Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
eftaxias, KUNSPECIFIEDUNSPECIFIED
cheong took, CUNSPECIFIEDUNSPECIFIED
venturini, BUNSPECIFIEDUNSPECIFIED
arscott, DUNSPECIFIEDUNSPECIFIED
Date : 14 May 2017
Funders : innovate uk
Grant Title : KTP project with WSP
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 04 Apr 2017 16:40
Last Modified : 04 Apr 2017 16:40
URI: http://epubs.surrey.ac.uk/id/eprint/813761

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