Petri neural network model for the effect of controlled thermomechanical process parameters on the mechanical properties of HSLA steels

被引：16
|
作者：
Datta, S [1 ]
Sil, J
Banerjee, MK
机构：
[1] BE Coll, Dept Met, Howrah 711103, India
[2] BE Coll, Dept Comp Sci & Technol, Howrah 711103, India
来源：
ISIJ INTERNATIONAL | 1999年 / 39卷 / 10期
关键词：
chemical composition; thermomechanical control processing; HSLA steel; petri neural network; back propagation;
D O I：
10.2355/isijinternational.39.986
中图分类号：
TF [冶金工业];
学科分类号：
0806 ;
摘要：
The effect of composition and controlled thermomechanical process parameters on the mechanical properties of HSLA steels is modelled using the Widrow-Hoff's concept of training a neural net with feed-forward topology by applying Rumelhart's back propagation type algorithm for supervised learning, using a Petri like net structure. The data used are from laboratory experiments as well as from the published literature. The results from the neural network are found to be consistent and in good agreement with the experimented results.
引用
收藏
页码：986 / 990
页数：5
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