TY - JOUR
T1 - A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
AU - Pettersson, F.
AU - Chakraborti, N.
AU - Saxén, H.
PY - 2007/1
Y1 - 2007/1
N2 - 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.
AB - 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.
KW - Artificial neural net
KW - Blast furnace
KW - Evolutionary computation
KW - Evolutionary multi-objective optimization
KW - Genetic algorithms
KW - Iron making
KW - predator-prey algorithm
UR - http://www.scopus.com/inward/record.url?scp=33750963282&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2005.09.001
DO - 10.1016/j.asoc.2005.09.001
M3 - Article
AN - SCOPUS:33750963282
SN - 1568-4946
VL - 7
SP - 387
EP - 397
JO - Applied Soft Computing
JF - Applied Soft Computing
IS - 1
ER -