Application of Model based on Artificial Immune RBF Neural Network to Predict Silicon Content in Hot metal

被引：1

作者：

Yang, Jia ^{[1
]}

Xu, Qiang ^{[2
]}

Cao, Changxiu ^{[1
]}

Ren, Jianghong ^{[1
]}

机构：

[1] Chongqing Univ, Coll Automat, Chongqing 400030, Peoples R China

[2] Chongqing Technol & Business Univ, Coll Comp Sci & Informat Engn, Chongqing 400067, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

RBF neural network; artificial immune; immune recognition; silicon content in hot metal;

D O I：

10.1109/WCICA.2008.4593109

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper studied a Radial Basis Function(RBF) network learning algorithm based on immune recognition principle. In the algorithm, the recognized data is regarded as antigens and the compression mapping of antigens as antibodies, i,e, the hidden layer centers. In order to improve convergence speed and precision of the RBF network, we adopt the least quare algorithm to determin the weights of the output layer. Applying the model to blast furnace of a large iron and steel Group Co., application result shows that the model possesses far superior forecast precision and requires less constructing time.

引用

页码：1290 / +

页数：2