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The hypersphere support vector machine is an efficient method to evaluate the state of mechanism. The state of Power-Shift Steering Transmission (PSST) is studied using hypersphere support vector machine. The theory of hypersphere support vector machine is researched and an evaluation model is developed. The generalization of the model is analyzed. The spectrometric oil analysis data are processed...
The hypersphere support vector machine is an efficient method to distinguish the state of mechanism. The state of Power-Shift Steering Transmission (PSST) is studied using hypersphere support vector machine and Principal Component Analysis(PCA) with spectrometric oil analysis data. The fundamental of hypersphere support vector machine and PCA is researched. The influence of model parameters for performance...
Spectrometric oil analysis technology is an important method in condition monitoring. This method has been applied to study the state of Power-shift Steering Transmission (PSST) in this paper. But, how to predict the future state of the PSST using existing data is a difficult work. In order to solve this problem, a support vector regression method is applied. The building process of this method is...
This paper is aimed at the condition monitoring problem of the Power-shift Steering Transmission (PSST), a method of multiple out least squares support vector regression is developed which is applied to prediction of spectrometric oil analysis data. Radial Basis Function (RBF) is used is this algorithm. There are two parameters ?? and ??2. The selection of ?? and ??2 is studied using cross validation...
Support vector machine (SVM) is an efficient method for data mining of oil analysis. The principle and structural risk of SVM are described in this paper. And the structural risk is studied using oil analysis data. During the process, parameters determination is a very important part because parameters have great influence on the performance of SVM. We select the Radial Basis Function (RBF) as the...
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