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Nonlinear time series fault prediction online based on incremental learning LS-SVM

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2 Author(s)
Zhanxin Zhou ; Dept. of Control Sci. & Eng., Tongji Univ., Shanghai ; Yongqi Chen

For nonlinear time series fault prediction online, an incremental learning least squares support machine (LSSVM) is presented to replace LS-SVM which is as a kind of regression method with good generalization ability and trained offline in batch way. The incremented learning LS-SVM fully utilizes historical training results and reduces memory and computation time, which guarantee to predict time series online. Two simulations results show that the incremental learning LSSVM has good performance for predicting nonlinear series fault prediction online.

Published in:

Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on

Date of Conference:

1-3 Sept. 2008