Optimization of back propagation algorithm and GAS-assisted ANN models for hot metal desulphurization
Abstract
Adaptive neural net (ANN) model of hot metal desulphurization is first optimized by various search methods including the golden section search and Davies-Swann-Campey methods. Logarithmic preprocessing of input data leads to a further improvement in generalization ability of the net. Genetic adaptive search (GAS) method is used to optimize the mathematical model for desulphurization and when the input data are preprocessed with this optimized model and fed into an artificial neural net, the generalization ability of the net becomes even better. Best results are obtained when using GAS to optimize the interconnection weights during the training phase, while training data are preprocessed through a mathematical model already optimized by GAS. For every process several options presented by a combination of ANN and GAS must be systematically investigated before choosing the ultimate model for predictions on shop floor.
Authors: Brahma Deo, Amlan Datta, Basant Kukreja, Ravi Rastogi, Kalyanmoy Deb
Published in: Steel Research (1994)