Application of optimizing the parameters of SVM using genetic simulated annealing algorithm

Cao, Lihua; Zhou, Shanquan; Li, Rui; Wu, Fan; Liu, Tao

doi:10.1109/wcica.2008.4593806

Cited by 2 publications

(2 citation statements)

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“…In the function, train T is the recognition rate of the training sample set that is equal to ( / ) In the function, * (2,1 )/ t randn α is a set searching step length, where α is set manually according to the amount of data, t is the temperature parameter [18] in the original SA physical model, and k refers to the iterations. In our problem, we call t as the step length controller.…”

Section: ) Objective Functionmentioning

confidence: 99%

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

Tang¹,

Zhuo²,

Wang³

et al. 2016

Journal of Electrical Engineering and Technology

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-With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultrahigh frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.

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Section: ) Objective Functionmentioning

confidence: 99%

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

Tang¹,

Zhuo²,

Wang³

et al. 2016

Journal of Electrical Engineering and Technology

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show abstract

“…Its structure is shown in Figure 4. 2) Renewal function for parameter p 1 * (2,1 )/ k k p p t randn α + = + (7) Here, * (2,1 )/ t randn α a set searching step length, where α is set manually according to the amount of data, t the temperature parameter [8] in the original SA physical model, and k the iterations. In our problem, we call t as the step length controller.…”

Section: B Dual-tree Classification For Insulation Defectsmentioning

confidence: 99%

SA-SVM incremental algorithm for GIS PD pattern recognition

Wang

Tang

Zhuo

et al. 2012

2012 International Conference on High Voltage Engineering and Application

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With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. Using ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge as inputs, a support vector machine (SVM) incremental method based on simulated annealing (SA) is constructed. Examples show that the new method speeds up the data update rate and improves the adaptability of the classifier.

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Application of optimizing the parameters of SVM using genetic simulated annealing algorithm

Cited by 2 publications

References 0 publications

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

SA-SVM incremental algorithm for GIS PD pattern recognition

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