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Based on principal component analysis (PCA) and support vector machine (SVM), a new method for the fault diagnosis of TE Process is proposed. The fault recognition based on kernel principal component analysis (KPCA) is analyzed and SVM is employed as a classifier for fault classification. To establish a more efficient SVM model, genetic algorithm (GA) is used to determine the optimal kernel parameter...
A soft fault diagnosis method for analog circuits based on support vector machine (SVM)is developed in this paper. SVM is a novel machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The multi-classification SVM methods including one versus rest, one versus one, and decision directed...
SVM for classification is sensitive to noise and multicollinearity between attributes. Correlative component analysis (CCA) was used to eliminated multicollinearity and noise of original sample data before classified by SVM. To improve the SVM performance, Eugenic Genetic Algorithm (EGA) was used to optimize the parameters of SVM. Finally, a typical example of two classes natural spearmint essence...
Kernel function plays a very important role in the performance of SVM. In order to improve generalization capability of SVM classifier, this paper proposes a new mechanism to optimize the parameters of combined kernel function by using large margin learning theory and a genetic algorithm, which aims to search the optimal parameters for the combined kernel function. This approach leads SVM to attain...
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