A genetic algorithms based multi-objective neural net applied to noisy blast furnace data

Authors:
F. Pettersson;N. Chakraborti;H. Saxén
Affiliations:
Faculty of Chemical Engineering, Heat Engineering Laboratory, íbo Akademy University, Biskopsgatan 8, FIN-20500 íbo, Finland;Department of Metallurgical & Materials Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, India;Faculty of Chemical Engineering, Heat Engineering Laboratory, íbo Akademy University, Biskopsgatan 8, FIN-20500 íbo, Finland
Venue:
Applied Soft Computing
Year:
2007

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Abstract

A genetic algorithms based multi-objective optimization technique was utilized in the training process of a feed forward neural network, using noisy data from an industrial iron blast furnace. The number of nodes in the hidden layer, the architecture of the lower part of the network, as well as the weights used in them were kept as variables, and a Pareto front was effectively constructed by minimizing the training error along with the network size. A predator-prey algorithm efficiently performed the optimization task and several important trends were observed.