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- W2376473314 abstract "In order to monitor the imperial smelting furnace(ISF)state in time and accurately diagnose the faults,a fault diagnosis approach based on kernel principal component analysis(KPCA)and multi-class classifiers of support vector machine(SVM)was proposed.The principle of the method was as follows:Firstly,the KPCA approach was adopted to extract the feature and the monitoring model was established.Secondly,the SVM multi-class classifiers with'one to other'algorithm was used for classification with the input of the feature.The experimental results show that,compared with the features extracted by principal component analysis(PCA),the proposed method increases the separability of the data set,performs better recognition ability,and it can be used in the imperial smelting furnace(ISF)fault diagnosis." @default.
- W2376473314 created "2016-06-24" @default.
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- W2376473314 date "2009-01-01" @default.
- W2376473314 modified "2023-09-24" @default.
- W2376473314 title "Method based on kernel principal component analysis and support vector machine and its application" @default.
- W2376473314 hasPublicationYear "2009" @default.
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