@inproceedings{99f193c8997843c7898f57dfe1db0df6,
title = "Nonlinear inertia convergence classification model of online power",
abstract = "The development and the progress of science and technology of the power industry is faster and faster. Electric power cables are getting more and more widely used in the power system. It plays an extremely important role in industrial production and modern life. To overcome the problem that the kernel parameter and the punishment factor have great influence on the quality of Support Vector Machine (SVM) model, the Particle Swarm Optimization (PSO) is used to optimize the parameters, and then a kind of Hybrid Method Support Vector Machine (HMSVM) is established for fault recognition. Finally, the HMSVM is applied to the recognition of online power cable faults. It is experimentally proved that, the HMSVM is correct and effective for the fault recognition of the online power cable.",
keywords = "Fault recognition, Hybrid model, Particle swarm optimization, Support vector machines",
author = "Mei Wang and Yanan Guo and Xiaowei Li and Wei Mo and Liang Wang",
year = "2014",
doi = "10.1109/IS3C.2014.170",
language = "英语",
isbn = "9781479952779",
series = "Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014",
publisher = "IEEE Computer Society",
pages = "630--633",
booktitle = "Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014",
note = "2nd International Symposium on Computer, Consumer and Control, IS3C 2014 ; Conference date: 10-06-2014 Through 12-06-2014",
}