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Modeling neural plasticity in echo state networks for time series prediction

Yusoff, M-H and Jin, Y (2014) Modeling neural plasticity in echo state networks for time series prediction In: 14th UK Workshop on Computational Intelligence (UKCI), 2014, 2014-09-08 - 2014-09-10, Bradford, UK.

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Abstract

In this paper, we investigate the influence of neural plasticity on the learning performance of echo state networks (ESNs) and supervised learning algorithms in training readout connections for two time series prediction problems including the sunspot time series and the Mackey Glass chaotic system. We implement two different plasticity rules that are expected to improve the prediction performance, namely, anti-Oja learning rule and the Bienenstock-Cooper-Munro (BCM) learning rule combined with both offline and online learning of the readout connections. Our experimental results have demonstrated that the neural plasticity can more significantly enhance the learning in offline learning than in online learning.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Yusoff, M-HUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 17 October 2014
Identification Number : 10.1109/UKCI.2014.6930163
Additional Information : © 2014 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.
Depositing User : Symplectic Elements
Date Deposited : 10 Dec 2014 17:42
Last Modified : 11 Dec 2014 02:33
URI: http://epubs.surrey.ac.uk/id/eprint/806892

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