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- W1977638553 abstract "In order to better study the wear state of vital parts of the large scale equipment, and overcoming the disadvantage of small sample of vital parts, we use the least squares support vector machine (LS_SVM) algorithm to predict the wear state of vital parts. Using of quantum particle swarm optimization (QPSO) to optimize parameters least squares support vector machine, and achieved good results. Compared those with the method that use of curve fitting to predict the data development trend, show that this method is superior to the curve fitting method, and has good application value." @default.
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- W1977638553 date "2014-07-01" @default.
- W1977638553 modified "2023-09-27" @default.
- W1977638553 title "Application of the Least Squares Support Vector Machine for Life Prediction of Vital Parts" @default.
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- W1977638553 doi "https://doi.org/10.4028/www.scientific.net/amm.584-586.2129" @default.
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