Dynamic temperature modeling of continuous annealing furnace using GGAP-RBF neural network

被引：22

作者：

Li, SY ^{[1
]}

Chen, Q

Huang, GB

机构：

[1] Shanghai Jiao Tong Univ, Inst Automat, Shanghai 200030, Peoples R China

[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

NEUROCOMPUTING | 2006年 / 69卷 / 4-6期

关键词：

continuous annealing furnace; quality control model; GGAP-RBF; sequential learning; neuron significance;

D O I：

10.1016/j.neucom.2005.01.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dynamic modeling of the quality control of a real large-scale continuous annealing process is studied in this paper. This continuous annealing process consists of several sub-processes and there exists unknown complex nonlinear mapping between the sub-process set points and the final annealing quality. The quality model should be constructed and updated based on the new data sequentially collected from the real process in order to optimize the set point of each sub-process dynamically. To meet this demand, a latest developed sequential learning algorithm called generalized growing and pruning RBF (GGAP-RBF) neural network is used to establish the required dynamic quality control model. Oil-line application of this quality model on the continuous annealing furnace in a steel factory has been conducted and the actual performance is as good as required. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：523 / 536

页数：14

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