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

被引：202
作者：
Pettersson, F.
Chakraborti, N. [1 ]
Saxen, H.
机构：
[1] Indian Inst Technol, Dept Met & Mat Engn, Kharagpur 721302, W Bengal, India
[2] Abo Akad Univ, Fac Chem Engn, Heat Engn Lab, FIN-20500 Turku, Finland
来源：
APPLIED SOFT COMPUTING | 2007年 / 7卷 / 01期
关键词：
genetic algorithms; artificial neural net; evolutionary computation; evolutionary multi-objective optimization; predator-prey algorithm; iron making; blast furnace; CLASSIFICATION; OPTIMIZATION; DESIGN;
D O I：
10.1016/j.asoc.2005.09.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：387 / 397
页数：11
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