Abstract
The E-core Transverse Flux Machine (ETFM) is combined of transverse flux and reluctance principle. An accurate ETFM model needs a good knowledge of the magnetic characteristics to know its electrical and mechanical behaviors. This paper proposed Support Vector Regression Machine (SVRM) and Artificial Neural Network (ANN) methods for determining of the magnetic characteristics of the ETFM. The data for the training and testing is obtained by experimental measurement methods. To reveal the accuracy of the nonlinear learning methods, the SVRM performance is compared with ANN's.
Keywords
Artificial neural network
Support vector machine
Magnetic flux
Transverse plane
Flux (metallurgy)
Magnetic reluctance
Core (optical fiber)
Computer science
Transverse magnetic
Nonlinear system
Artificial intelligence
Machine learning
Physics
Magnetic field
Engineering
Magnet
Mechanical engineering
Optics
Materials science
Structural engineering
Subject Areas
Non-Destructive Testing Techniques
·Mechanical Engineering ·Physical Sciences
Magnetic Properties and Applications
·Electronic, Optical and Magnetic Materials ·Physical Sciences
Electric Motor Design and Analysis
·Electrical and Electronic Engineering ·Physical Sciences
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