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Fast and efficient sequential learning algorithms using direct-link RBF networks

Asirvadam , Vijanth Sagayan and McLoone, Sean and Irwin, George (2003) Fast and efficient sequential learning algorithms using direct-link RBF networks. In: Neural Networks For Signal Processing XIII. NEURAL NETWORKS For SIGNAL PROCESSING (XIII). IEEE Press, Piscataway, New Jersey, pp. 209-218. ISBN 0-7803-8178-5

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Official URL: http://isp.imm.dtu.dk/nnsp2003

Abstract

Novel fast and efficient sequential learning algorithms are proposed for direct-link radial basis function (DRBF) networks. The dynamic DRBF network is trained using the recently proposed decomposed/parallel recursive Levenberg Marquardt (PRLM) algorithm by neglecting the interneuron weight interactions. The resulting sequential learning approach enables weights to be updated in an efficient parallel manner and facilitates a minimal update extension for real-time applications. Simulation results for two benchmark problems show the feasibility of the new training algorithms.

Item Type:Book Section
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:3828
Deposited By: Dr Vijanth Sagayan Asirvadam
Deposited On:04 Jan 2011 00:42
Last Modified:04 Jan 2011 00:42

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