Vertical quench furace Hammerstein fault predicting model based on least squares support vector machine and its application

Authors:
Shao-Hua Jiang;Wei-Hua Gui;Chun-Hua Yang
Affiliations:
School of Information Science and Engineering, Central South University, Changsha, China and School of Computer Science, Shaoguan University, Shaoguan, China;School of Information Science and Engineering, Central South University, Changsha, China;School of Information Science and Engineering, Central South University, Changsha, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Year:
2009

Citing 3
Cited 0

Brief paper: Iterative identification of Hammerstein systems

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A blind approach to Hammerstein model identification

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Identification of MIMO Hammerstein models using least squares support vector machines

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Abstract

Since large-scale vertical quench furnace is voluminous, whose working condition is a typically complex process with distributed parameter, nonlinear, multi-inputs/multi-outputs, close coupled variables, etc, Hammerstein model of the furnace is presented. Firstly, the nonlinear function of Hammerstein model is constructed by least squares support vector machines regression. A numerical algorithm for subspace system (singular value decomposition, SVD) is utilized to identify the Hammerstein model. Finally, the model is used to predict the furnace temperature. The simulation research shows this model provides accurate prediction and is with desirable application value.