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.
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Acknowledgements
This work is supported by the Project on Research and Demonstration of Application Technology of Intelligent Management and Dynamic Simulation Based on Bottom-guaranteed Power Grid Communication System, Under Grant No. GDKJXM20180249 (036000KK52180006).
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Shi, Z., Zeng, Y., Zhang, Z., Hu, T. (2021). Fault Diagnosis Algorithm for Power Bottom-Guaranteed Communication Network Based on Random Forest. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_81
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DOI: https://doi.org/10.1007/978-981-15-3753-0_81
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