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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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
Depositing User: Dr Vijanth Sagayan Asirvadam
Date Deposited: 12 Jan 2011 03:55
Last Modified: 12 Jan 2011 03:55
URI: http://scholar.utp.edu.my/id/eprint/3957

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