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

F. Pettersson, N. Chakraborti*, H. Saxén

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

202 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)387-397
Number of pages11
JournalApplied Soft Computing Journal
Volume7
Issue number1
DOIs
Publication statusPublished - Jan 2007
MoE publication typeA1 Journal article-refereed

Keywords

  • Artificial neural net
  • Blast furnace
  • Evolutionary computation
  • Evolutionary multi-objective optimization
  • Genetic algorithms
  • Iron making
  • predator-prey algorithm

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