期刊
APPLIED SOFT COMPUTING
卷 7, 期 1, 页码 387-397出版社
ELSEVIER
DOI: 10.1016/j.asoc.2005.09.001
关键词
genetic algorithms; artificial neural net; evolutionary computation; evolutionary multi-objective optimization; predator-prey algorithm; iron making; blast furnace
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. (C) 2005 Elsevier B.V. All rights reserved.
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