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- W3040572743 abstract "In order to solve the problem of low performance of power bottom-guaranteed communication network fault diagnosis algorithm, this paper proposes a fault diagnosis algorithm for power bottom-guaranteed communication network based on random forest. Firstly, combined with the characteristics of the network management data of power bottom-guaranteed communication network, it is demonstrated that the random forest algorithm has higher execution efficiency and better anti-noise ability by comparing the existing machine learning algorithms, when solving the fault diagnosis problem of power bottom-guaranteed communication network. Secondly, according to the characteristics of the random forest algorithm, the existing power communication equipment data sets are preprocessed to generate training sets and test sets, and the selection strategy is used to optimize the over-fitting problem to generate decision tree data set for algorithm execution. Finally, the data set is gradually classified by iterative method, and the best classification of the data set is solved based on the classification result of decision tree. By comparing with the existing algorithms, it is verified that the proposed algorithm improves the performance of power bottom-guaranteed communication network fault diagnosis algorithm." @default.
- W3040572743 created "2020-07-10" @default.
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- W3040572743 date "2020-07-01" @default.
- W3040572743 modified "2023-09-25" @default.
- W3040572743 title "Fault Diagnosis Algorithm for Power Bottom-Guaranteed Communication Network Based on Random Forest" @default.
- W3040572743 cites W2044694860 @default.
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- W3040572743 doi "https://doi.org/10.1007/978-981-15-3753-0_81" @default.
- W3040572743 hasPublicationYear "2020" @default.
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