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Abstract: Least
squares support vector machines (LS-SVM) is a perfect model-learning
algorithm with good accuracy and high speed. In order to solve the
online multivariable-modeling problem of a Lithopone calcination process
in rotary kiln, a new kind of online modeling algorithm based on time
window in LS-SVM is proposed. The purpose is to show its powerful
identification performances. First, the main mechanism of LS-SVM is
presented, and then the optimization algorithm of the time window is
discussed. The current feature of the model has strong relationship with
L updated data. Karush-Kuhn-Tucker (KKT) optimization condition decides
whether to do the retraining at each updating procedure and avoids
unnecessary recalculations. Finally, the simulation results are
presented. The good performance of this algorithm shows its broad
prospect on dynamic identifications of complex nonlinear processes.
Key
words: Least
squares support vector machines(LS-SVM) Online modeling Time
window Kernels
CLC No: TP18
广东省科技厅(C10909)和广州市科技局(2003Z3-D0091)资助项目.
Received 20040716, received in revised form 20041012
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