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- W2369337823 abstract "Applying data mining methods to extract the appropriate fault diagnosis knowledge from the real-time database is an effective way,also is an practical significance and research value problem.In order to raise the efficiency of fault diagnosis of steam turbine units and consider its costs and complexity,use the correlation analysis as data pre-processor.Calculate the correlation coefficients between attributes,and combine with max-min distance,then keep only one of the attributes which most highly correlates.Then construct support vector machine classifier,applying particle swarm optimization to find optimal parameter.Experimental results show that SVM outperforms linear discriminant analysis(LDA) and back-propagation neural networks(BPN) in classification performance and can be well applied in fault diagnosis." @default.
- W2369337823 created "2016-06-24" @default.
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- W2369337823 date "2012-01-01" @default.
- W2369337823 modified "2023-09-23" @default.
- W2369337823 title "SVM Fault Diagnosis Research Based on Attribute Reduction and Parameters Optimization" @default.
- W2369337823 hasPublicationYear "2012" @default.
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