scholarly journals Contamination degree prediction of insulator surface based on exploratory factor analysis‐least square support vector machine combined model

High Voltage ◽  
2020 ◽  
Author(s):  
Jiaxiang Sun ◽  
Hongru Zhang ◽  
Qingquan Li ◽  
Hongshun Liu ◽  
Xinbo Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Least Square ◽  
Support Vector ◽  
Combined Model ◽  
Insulator Surface ◽  
Contamination Degree

Research of GPS Elevation Conversion Based on Least Square Support Vector Machine and BP Neural Network

2014 ◽  
Vol 501-504 ◽  
pp. 2166-2171 ◽  
Author(s):  
Li Long Liu ◽  
Teng Xu Zhang ◽  
Miao Zhou ◽  
Wei Wang ◽  
Liang Ke Huang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bp Neural Network ◽  
Curve Surface ◽  
Least Square ◽  
Support Vector ◽  
Combined Model ◽  
Polynomial Curve ◽  
Optimal Weighting ◽  
Bp Neural Network Model

This paper proposed the optical weighting combined mode of Least Square Support Vector Machine (LS-SVM) and BP Neural network. According to the measured data, this paper compared and analyzed the accuracy of LS-SVM model, BP Neural network model; quadratic polynomial curve surface fitting based on Total least-square algorithm and optimal weighting combined model, the data shows that the optimal weighting combined model has higher precision then others.

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

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