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Fast sequential learning methods on RBF-network using decomposed training algorithms

Asirvadam , Vijanth Sagayan and McLoone, Sean and Irwin, George W (2004) Fast sequential learning methods on RBF-network using decomposed training algorithms. In: IEEE International Symposium on Intelligent Control, 2004. , 2-4 September 2004, Taiwan.

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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

Abstract

This work investigates novel sequential learning methods applied on a decomposed form of training algorithms using radial basis function (RBF) network. The dynamic expansion of RBF network by adding neurons to the hidden layer during the course of training facilitates the weight update to be decomposed on neuron by neuron basis. The fast or minimal update approach which can be adopted with ease on a decomposed algorithms are also presented in This work.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:4028
Deposited By: Dr Vijanth Sagayan Asirvadam
Deposited On:17 Jan 2011 01:33
Last Modified:17 Jan 2011 01:33

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