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Separable recursive training algorithms for feedforward neural networks

Asirvadam , Vijanth Sagayan and McLoone, Sean and Irwin, George W (2002) Separable recursive training algorithms for feedforward neural networks. In: Proceedings of the 2002 International Joint Conference on Neural Networks,IJCNN '02., 12-17 May 2002, Honolulu, HI , USA .

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

Abstract

Novel separable recursive training strategies are derived for the training of feedforward neural networks. These hybrid algorithms combine nonlinear recursive optimization of hidden-layer nonlinear weights with recursive least-squares optimization of linear output-layer weights in one integrated routine. Experimental results for two benchmark problems demonstrate the superiority of the new hybrid training schemes compared to conventional counterparts

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:3957
Deposited By: Dr Vijanth Sagayan Asirvadam
Deposited On:12 Jan 2011 03:55
Last Modified:12 Jan 2011 03:55

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