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- W4308703608 abstract "When the fuzzy factors is in the sample or noise around the classification surface which is the decisive factor for the summary of support vector machines (SVM), the operation results of SVM will be greatly affected. In this case, the noise must be effectively removed to minimize the impact on the sample. Here, the method of “one class to the rest class” is used to establish fault classifier, and a decision algorithm combining binary tree with fuzzy support vector machine is proposed. In this algorithm, the noise points near the classification surface in the samples are removed by SDWFCM (Spacial distance weighted fuzzy C-Means) method firstly. Then, based on the fuzzy feature representation method, the fuzzy support vector machine is improved by combining the fuzzy membership degree determination method and the basis of the threshold determination after fuzzy programming. The support vector machine can effectively process the composite samples of mixed fuzzy samples, in order to accomplish the fault diagnosis of a certain type of equipment." @default.
- W4308703608 created "2022-11-14" @default.
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- W4308703608 date "2022-11-11" @default.
- W4308703608 modified "2023-09-29" @default.
- W4308703608 title "An equipment fault diagnosis method based on fuzzy support vector machine" @default.
- W4308703608 cites W2138402108 @default.
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- W4308703608 doi "https://doi.org/10.1117/12.2641577" @default.
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