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 |
| 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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