Online Kernel Extreme Learning Machine and Its Application to Time Series Prediction

MA Chao; ZHANG Yingtang

doi:10.13976/j.cnki.xk.2014.0624

Volume 43 Issue 5

Oct. 2014

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INFORMATION AND CONTROL > 2014 > 43(5): 624-629. > DOI: 10.13976/j.cnki.xk.2014.0624 CSTR: 32166.14.xk.2014.0624

MA Chao, ZHANG Yingtang. Online Kernel Extreme Learning Machine and Its Application to Time Series Prediction[J]. INFORMATION AND CONTROL, 2014, 43(5): 624-629. DOI: 10.13976/j.cnki.xk.2014.0624

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MA Chao, ZHANG Yingtang. Online Kernel Extreme Learning Machine and Its Application to Time Series Prediction[J]. INFORMATION AND CONTROL, 2014, 43(5): 624-629. DOI: 10.13976/j.cnki.xk.2014.0624

Citation:

MA Chao, ZHANG Yingtang. Online Kernel Extreme Learning Machine and Its Application to Time Series Prediction[J]. INFORMATION AND CONTROL, 2014, 43(5): 624-629. DOI: 10.13976/j.cnki.xk.2014.0624

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Online Kernel Extreme Learning Machine and Its Application to Time Series Prediction

MA Chao^,,
ZHANG Yingtang

Seventh Department, Ordnance Engineering College, Shijiazhuang 050003, China

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Received Date: August 02, 2013
Revised Date: November 10, 2013
Published Date: October 19, 2014

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Abstract

Abstract

To monitor the running state of hydraulic equipment online and in real-time by using new, sequentially input samples, we propose an online kernel extreme learning machine (OL-KELM) algorithm. OL-KELM applies Cholesky factorization to extend the kernel extreme learning machine (KELM) from offline mode to online mode. Unlike KELM, OL-KELM can complete the training recursively on the basis of new samples. The learning efficiency is improved by replacing the matrix inverse operation with arithmetic in OL-KELM. Simulations of the online prediction for the chaotic time series show that OL-KELM can achieve the same accuracy as KELM with 40% to 60% less training time. Results of the online and real-time monitoring of a hydraulic pump by OL-KELM show that OL-KELM is robust, with a prediction error 28.3% to 46.2% of SRELM (sequential regularized extreme learning machine). OL-KELM is proved effective for online, real-time monitoring of hydraulic equipment.
- kernel extreme learning machine(KELM),
- time series prediction,
- hydraulic pump,
- state monitoring,
- robustness

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