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

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

*Korresponderande författare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

202 Citeringar (Scopus)

Sammanfattning

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.

OriginalspråkEngelska
Sidor (från-till)387-397
Antal sidor11
TidskriftApplied Soft Computing Journal
Volym7
Utgåva1
DOI
StatusPublicerad - jan 2007
MoE-publikationstypA1 Tidskriftsartikel-refererad

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