Modeling a two-phase excitation switched reluctance motor with artificial neural network

This paper first introduces the necessity to adopt feed-forward (FF) artificial neural network (ANN) in approximation of magnetic characteristics for a two-phase excitation (TPE) switched reluctance motor (SRM) modeling. Then the magnetic characteristics of a TPESRM are trained by a learning algorit...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Guo Wei, Zhang Haitao, Zhao Zhengming, Zhan Qionghua
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Artificial neural networks Feedforward systems Inductance Least squares approximation Magnetic analysis Magnetic switching Neural networks Performance analysis Reluctance machines Reluctance motors
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	1012 Vol.2
container_issue
container_start_page	1009
container_title
container_volume	2
creator	Guo Wei Zhang Haitao Zhao Zhengming Zhan Qionghua
description	This paper first introduces the necessity to adopt feed-forward (FF) artificial neural network (ANN) in approximation of magnetic characteristics for a two-phase excitation (TPE) switched reluctance motor (SRM) modeling. Then the magnetic characteristics of a TPESRM are trained by a learning algorithm named MARQUARDT algorithm. The first step of the training is the selection of net structure and learning algorithm. Then the preparations of the sample data are explained. Its main objective is to reduce the total number of samples effectively. Finally, the forward, inverse flux-linkage characteristics and the co-energy characteristics are successfully trained. The training results are acceptable for engineering applications.
format	Conference Proceeding
fullrecord	<record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_1375862</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1375862</ieee_id><sourcerecordid>1375862</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-7a5947f79e2a027bb4ada3a054c32b0dd6e6e1896f247af7825509a8fa9076193</originalsourceid><addsrcrecordid>eNotjMtKxDAUQAMijIz9Ajf5gUIeTW6ylMEXjLhRcDfcpjfTaKcd0gyjf29Rz-YsDpwLVnlwYKww0llvVqya5w-xoL3xWl6x9-epoyGNe468nKf62ONMnL5CKljSNPL5nEroqeOZhlMoOAbih6lMmS-h55hLiikkHPhIp_yr5ZM_r9llxGGm6t9r9nZ_97p5rLcvD0-b222dJJhSAxrfQARPCoWCtm2wQ43CNEGrVnSdJUvSeRtVAxjBKWOERxfRC7DS6zW7-fsmItodczpg_t5JDcZZpX8A4qdMZQ</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Modeling a two-phase excitation switched reluctance motor with artificial neural network</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Guo Wei ; Zhang Haitao ; Zhao Zhengming ; Zhan Qionghua</creator><creatorcontrib>Guo Wei ; Zhang Haitao ; Zhao Zhengming ; Zhan Qionghua</creatorcontrib><description>This paper first introduces the necessity to adopt feed-forward (FF) artificial neural network (ANN) in approximation of magnetic characteristics for a two-phase excitation (TPE) switched reluctance motor (SRM) modeling. Then the magnetic characteristics of a TPESRM are trained by a learning algorithm named MARQUARDT algorithm. The first step of the training is the selection of net structure and learning algorithm. Then the preparations of the sample data are explained. Its main objective is to reduce the total number of samples effectively. Finally, the forward, inverse flux-linkage characteristics and the co-energy characteristics are successfully trained. The training results are acceptable for engineering applications.</description><identifier>ISBN: 9787560518695</identifier><identifier>ISBN: 7560518699</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Feedforward systems ; Inductance ; Least squares approximation ; Magnetic analysis ; Magnetic switching ; Neural networks ; Performance analysis ; Reluctance machines ; Reluctance motors</subject><ispartof>The 4th International Power Electronics and Motion Control Conference, 2004. IPEMC 2004, 2004, Vol.2, p.1009-1012 Vol.2</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1375862$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1375862$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Guo Wei</creatorcontrib><creatorcontrib>Zhang Haitao</creatorcontrib><creatorcontrib>Zhao Zhengming</creatorcontrib><creatorcontrib>Zhan Qionghua</creatorcontrib><title>Modeling a two-phase excitation switched reluctance motor with artificial neural network</title><title>The 4th International Power Electronics and Motion Control Conference, 2004. IPEMC 2004</title><addtitle>IPEMC</addtitle><description>This paper first introduces the necessity to adopt feed-forward (FF) artificial neural network (ANN) in approximation of magnetic characteristics for a two-phase excitation (TPE) switched reluctance motor (SRM) modeling. Then the magnetic characteristics of a TPESRM are trained by a learning algorithm named MARQUARDT algorithm. The first step of the training is the selection of net structure and learning algorithm. Then the preparations of the sample data are explained. Its main objective is to reduce the total number of samples effectively. Finally, the forward, inverse flux-linkage characteristics and the co-energy characteristics are successfully trained. The training results are acceptable for engineering applications.</description><subject>Artificial neural networks</subject><subject>Feedforward systems</subject><subject>Inductance</subject><subject>Least squares approximation</subject><subject>Magnetic analysis</subject><subject>Magnetic switching</subject><subject>Neural networks</subject><subject>Performance analysis</subject><subject>Reluctance machines</subject><subject>Reluctance motors</subject><isbn>9787560518695</isbn><isbn>7560518699</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjMtKxDAUQAMijIz9Ajf5gUIeTW6ylMEXjLhRcDfcpjfTaKcd0gyjf29Rz-YsDpwLVnlwYKww0llvVqya5w-xoL3xWl6x9-epoyGNe468nKf62ONMnL5CKljSNPL5nEroqeOZhlMoOAbih6lMmS-h55hLiikkHPhIp_yr5ZM_r9llxGGm6t9r9nZ_97p5rLcvD0-b222dJJhSAxrfQARPCoWCtm2wQ43CNEGrVnSdJUvSeRtVAxjBKWOERxfRC7DS6zW7-fsmItodczpg_t5JDcZZpX8A4qdMZQ</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Guo Wei</creator><creator>Zhang Haitao</creator><creator>Zhao Zhengming</creator><creator>Zhan Qionghua</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2004</creationdate><title>Modeling a two-phase excitation switched reluctance motor with artificial neural network</title><author>Guo Wei ; Zhang Haitao ; Zhao Zhengming ; Zhan Qionghua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-7a5947f79e2a027bb4ada3a054c32b0dd6e6e1896f247af7825509a8fa9076193</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Artificial neural networks</topic><topic>Feedforward systems</topic><topic>Inductance</topic><topic>Least squares approximation</topic><topic>Magnetic analysis</topic><topic>Magnetic switching</topic><topic>Neural networks</topic><topic>Performance analysis</topic><topic>Reluctance machines</topic><topic>Reluctance motors</topic><toplevel>online_resources</toplevel><creatorcontrib>Guo Wei</creatorcontrib><creatorcontrib>Zhang Haitao</creatorcontrib><creatorcontrib>Zhao Zhengming</creatorcontrib><creatorcontrib>Zhan Qionghua</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guo Wei</au><au>Zhang Haitao</au><au>Zhao Zhengming</au><au>Zhan Qionghua</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Modeling a two-phase excitation switched reluctance motor with artificial neural network</atitle><btitle>The 4th International Power Electronics and Motion Control Conference, 2004. IPEMC 2004</btitle><stitle>IPEMC</stitle><date>2004</date><risdate>2004</risdate><volume>2</volume><spage>1009</spage><epage>1012 Vol.2</epage><pages>1009-1012 Vol.2</pages><isbn>9787560518695</isbn><isbn>7560518699</isbn><abstract>This paper first introduces the necessity to adopt feed-forward (FF) artificial neural network (ANN) in approximation of magnetic characteristics for a two-phase excitation (TPE) switched reluctance motor (SRM) modeling. Then the magnetic characteristics of a TPESRM are trained by a learning algorithm named MARQUARDT algorithm. The first step of the training is the selection of net structure and learning algorithm. Then the preparations of the sample data are explained. Its main objective is to reduce the total number of samples effectively. Finally, the forward, inverse flux-linkage characteristics and the co-energy characteristics are successfully trained. The training results are acceptable for engineering applications.</abstract><pub>IEEE</pub></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISBN: 9787560518695
ispartof	The 4th International Power Electronics and Motion Control Conference, 2004. IPEMC 2004, 2004, Vol.2, p.1009-1012 Vol.2
issn
language	eng
recordid	cdi_ieee_primary_1375862
source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Artificial neural networks Feedforward systems Inductance Least squares approximation Magnetic analysis Magnetic switching Neural networks Performance analysis Reluctance machines Reluctance motors
title	Modeling a two-phase excitation switched reluctance motor with artificial neural network
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T16%3A27%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Modeling%20a%20two-phase%20excitation%20switched%20reluctance%20motor%20with%20artificial%20neural%20network&rft.btitle=The%204th%20International%20Power%20Electronics%20and%20Motion%20Control%20Conference,%202004.%20IPEMC%202004&rft.au=Guo%20Wei&rft.date=2004&rft.volume=2&rft.spage=1009&rft.epage=1012%20Vol.2&rft.pages=1009-1012%20Vol.2&rft.isbn=9787560518695&rft.isbn_list=7560518699&rft_id=info:doi/&rft_dat=%3Cieee_6IE%3E1375862%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=1375862&rfr_iscdi=true