Separable recursive training algorithms for feedforward neural networks
Vijanth Sagayan Asirvadam, Seán McLoone, G.W. Irwin · 2003
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.