NDLI: A new method based on MTL and LS-SVM for crosstalk predicting in electric vehicle

Content Provider	IEEE Xplore Digital Library
Author	Sun Tie-lei Lin Cheng
Copyright Year	2010
Description	Author affiliation: National Engineering Laboratory for Electric Vehicle, Mechanism and Automobile Engineering College, Beijing Institute of Technology, 100081, China (Sun Tie-lei; Lin Cheng)
Abstract	In electric vehicle crosstalk plays an important role in electromagnetic compatibility. However, the influence factors of crosstalk of lines in electric vehicle are very complex and variable. To get higher precision prediction, as many of the factors as possible are input in the forecast model in predicting computing. Multi-conductor transmission line (MTL) is one of multivariate electromagnetic analysis, which achieves parsimony and reduces dimensionality to simplify computation by extracting the smallest number of irrelevant components with little loss of information. In this paper, a new method for crosstalk predicting based on MTL and least squares support vector machine (LS-SVM) is presented. Firstly components are extracted from various factors of crosstalk by MTL and to be inputs of LS-SVM. Then LS-SVM is applied to train and forecasting. The model is characterized by simple computing. Analysis of the experimental results proved that the method proposed achieved greater accuracy and efficiency than conventional LS-SVM.
Starting Page	1503
Ending Page	1507
File Size	384688
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5582796
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Power transmission lines influence factors crosstalk Least squares support vector machine Crosstalk electric vehicle Electric vehicles Conductors multi-conductor transmission lines Wire Kernel
Content Type	Text
Resource Type	Article

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