Study of the agent quantum control model in hot metal desulphurization process based on Rough Set and GA-RBF nerve network

Author

Zhang, Yong ; Wang, Yukun ; Cang, Liang

Author_Institution

Sch. of Electron. & Inf. Eng., Univ. of Sci. & Technol., Anshan

fYear

2008

fDate

25-27 June 2008

Firstpage

2507

Lastpage

2511

Abstract

In view of low precision and low auto-adapted ability in traditional desulphurization control model, according to the mechanism of hot metal desulphurization process, the RBF nerve network desulphurization agent quantum model based on rough set and genetic algorithm is introduced. This model uses rough set to clean modeling data, uses genetic algorithm to select RBF network structure, and then introduces generalization error to the network training process. The emulate contrast shows the mathematical model can suffice for the requirement of hot metal desulphurization control process.

Keywords

genetic algorithms; metallurgical industries; neurocontrollers; process control; radial basis function networks; rough set theory; sulphur; RBF nerve network; agent quantum control model; generalization error; genetic algorithm; hot metal desulphurization control process; mathematical model; network training process; rough set; Automation; Counting circuits; Electronic mail; Genetic algorithms; Genetic engineering; Intelligent control; Mathematical model; Process control; Radial basis function networks; Desulphurization; Genetic Algorithm; RBF nerve network; Rough Set;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4593318

Filename

4593318