Asirvadam, Vijanth S. and McLoone, Sean F. and Irwin, George W. (2005) Computationally efficient sequential learning algorithms for direct link resource-allocating networks. Neurocomputing, 69 (1-3). pp. 142-157.

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Abstract

Computationally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). These are achieved by decomposing existing recursive training algorithms on a layer by layer and neuron by neuron basis. This allows network weights to be updated in an efficient parallel manner and facilitates the implementation of minimal update extensions that yield a significant reduction in computation load per iteration compared to existing sequential learning methods employed in resource-allocation network (RAN) and minimal RAN (MRAN) approaches. The new algorithms, which also incorporate a pruning strategy to control network growth, are evaluated on three different system identification benchmark problems and shown to outperform existing methods both in terms of training error convergence and computational efficiency.

Item Type:	Article
Keywords:	System identification; Radial basis functions; Extended Kalman Filter; Resource allocatingnetwork.
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	685
Depositing User:	Sean McLoone
Date Deposited:	23 Aug 2007
Journal or Publication Title:	Neurocomputing
Publisher:	Elsevier
Refereed:	Yes
URI:	http://www.elsevier.com/wps/find/journal...
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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