Loading…

Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems

The identification issues of a multivariable system with coloured noises are investigated in this paper. The decomposition strategy is utilised for dimension reduction by transforming a multivariable system into several sub-models. A maximum likelihood least squares-based iterative identification ap...

Full description

Saved in:
Bibliographic Details
Published in:International journal of systems science 2020-12, Vol.51 (16), p.3285-3298
Main Authors: Xia, Huafeng, Xie, Li, Zhu, Quanmin
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c338t-b379c7dfaa622a4add32d33843c9f746057def1cfd0bae8eb2b1389055050a853
cites cdi_FETCH-LOGICAL-c338t-b379c7dfaa622a4add32d33843c9f746057def1cfd0bae8eb2b1389055050a853
container_end_page 3298
container_issue 16
container_start_page 3285
container_title International journal of systems science
container_volume 51
creator Xia, Huafeng
Xie, Li
Zhu, Quanmin
description The identification issues of a multivariable system with coloured noises are investigated in this paper. The decomposition strategy is utilised for dimension reduction by transforming a multivariable system into several sub-models. A maximum likelihood least squares-based iterative identification approach is presented to enhance the parameter estimation accuracy by combining the iterative identification technique with the maximum likelihood principle. A simulation example is offered to test the effectiveness of the proposed method.
doi_str_mv 10.1080/00207721.2020.1814893
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1080_00207721_2020_1814893</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2476106901</sourcerecordid><originalsourceid>FETCH-LOGICAL-c338t-b379c7dfaa622a4add32d33843c9f746057def1cfd0bae8eb2b1389055050a853</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRS0EEuXxCUiWWKeM7Tx3oIqXBGIDa2sS260hiYudFPr3OLRsWXmsOXPtOYRcMJgzKOEKgENRcDbnsZizkqVlJQ7IjKV5mmSCVYdkNjHJBB2TkxDeASDLOMxI_4zfths72toP3dqVc4raQXsc7EZTq3Q_WGObeHU9xfXaO2xWOlDjPO3GNlLoLdatpvpz_KUS7f3UdBvbLyluYtZS07ANg-7CGTky2AZ9vj9Pydvd7eviIXl6uX9c3DwljRDlkNSiqJpCGcScc0xRKcFV7KSiqUyR5pAVShvWGAU16lLXvGairOJOkAGWmTgll7vc-OHPUYdBvrvR9_FJydMiZ5BXwCKV7ajGuxC8NnLtbYd-KxnISa38UysntXKvNs5d7-ZsHz10-OV8q-SA29Z547FvbJDi_4gftvSC-g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2476106901</pqid></control><display><type>article</type><title>Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems</title><source>Taylor and Francis Science and Technology Collection</source><creator>Xia, Huafeng ; Xie, Li ; Zhu, Quanmin</creator><creatorcontrib>Xia, Huafeng ; Xie, Li ; Zhu, Quanmin</creatorcontrib><description>The identification issues of a multivariable system with coloured noises are investigated in this paper. The decomposition strategy is utilised for dimension reduction by transforming a multivariable system into several sub-models. A maximum likelihood least squares-based iterative identification approach is presented to enhance the parameter estimation accuracy by combining the iterative identification technique with the maximum likelihood principle. A simulation example is offered to test the effectiveness of the proposed method.</description><identifier>ISSN: 0020-7721</identifier><identifier>EISSN: 1464-5319</identifier><identifier>DOI: 10.1080/00207721.2020.1814893</identifier><language>eng</language><publisher>London: Taylor &amp; Francis</publisher><subject>decomposition ; iterative identification ; Iterative methods ; maximum likelihood ; Multivariable system ; Parameter estimation ; Parameter identification</subject><ispartof>International journal of systems science, 2020-12, Vol.51 (16), p.3285-3298</ispartof><rights>2020 Informa UK Limited, trading as Taylor &amp; Francis Group 2020</rights><rights>2020 Informa UK Limited, trading as Taylor &amp; Francis Group</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-b379c7dfaa622a4add32d33843c9f746057def1cfd0bae8eb2b1389055050a853</citedby><cites>FETCH-LOGICAL-c338t-b379c7dfaa622a4add32d33843c9f746057def1cfd0bae8eb2b1389055050a853</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Xia, Huafeng</creatorcontrib><creatorcontrib>Xie, Li</creatorcontrib><creatorcontrib>Zhu, Quanmin</creatorcontrib><title>Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems</title><title>International journal of systems science</title><description>The identification issues of a multivariable system with coloured noises are investigated in this paper. The decomposition strategy is utilised for dimension reduction by transforming a multivariable system into several sub-models. A maximum likelihood least squares-based iterative identification approach is presented to enhance the parameter estimation accuracy by combining the iterative identification technique with the maximum likelihood principle. A simulation example is offered to test the effectiveness of the proposed method.</description><subject>decomposition</subject><subject>iterative identification</subject><subject>Iterative methods</subject><subject>maximum likelihood</subject><subject>Multivariable system</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><issn>0020-7721</issn><issn>1464-5319</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRS0EEuXxCUiWWKeM7Tx3oIqXBGIDa2sS260hiYudFPr3OLRsWXmsOXPtOYRcMJgzKOEKgENRcDbnsZizkqVlJQ7IjKV5mmSCVYdkNjHJBB2TkxDeASDLOMxI_4zfths72toP3dqVc4raQXsc7EZTq3Q_WGObeHU9xfXaO2xWOlDjPO3GNlLoLdatpvpz_KUS7f3UdBvbLyluYtZS07ANg-7CGTky2AZ9vj9Pydvd7eviIXl6uX9c3DwljRDlkNSiqJpCGcScc0xRKcFV7KSiqUyR5pAVShvWGAU16lLXvGairOJOkAGWmTgll7vc-OHPUYdBvrvR9_FJydMiZ5BXwCKV7ajGuxC8NnLtbYd-KxnISa38UysntXKvNs5d7-ZsHz10-OV8q-SA29Z547FvbJDi_4gftvSC-g</recordid><startdate>20201209</startdate><enddate>20201209</enddate><creator>Xia, Huafeng</creator><creator>Xie, Li</creator><creator>Zhu, Quanmin</creator><general>Taylor &amp; Francis</general><general>Taylor &amp; Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20201209</creationdate><title>Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems</title><author>Xia, Huafeng ; Xie, Li ; Zhu, Quanmin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-b379c7dfaa622a4add32d33843c9f746057def1cfd0bae8eb2b1389055050a853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>decomposition</topic><topic>iterative identification</topic><topic>Iterative methods</topic><topic>maximum likelihood</topic><topic>Multivariable system</topic><topic>Parameter estimation</topic><topic>Parameter identification</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xia, Huafeng</creatorcontrib><creatorcontrib>Xie, Li</creatorcontrib><creatorcontrib>Zhu, Quanmin</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of systems science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xia, Huafeng</au><au>Xie, Li</au><au>Zhu, Quanmin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems</atitle><jtitle>International journal of systems science</jtitle><date>2020-12-09</date><risdate>2020</risdate><volume>51</volume><issue>16</issue><spage>3285</spage><epage>3298</epage><pages>3285-3298</pages><issn>0020-7721</issn><eissn>1464-5319</eissn><abstract>The identification issues of a multivariable system with coloured noises are investigated in this paper. The decomposition strategy is utilised for dimension reduction by transforming a multivariable system into several sub-models. A maximum likelihood least squares-based iterative identification approach is presented to enhance the parameter estimation accuracy by combining the iterative identification technique with the maximum likelihood principle. A simulation example is offered to test the effectiveness of the proposed method.</abstract><cop>London</cop><pub>Taylor &amp; Francis</pub><doi>10.1080/00207721.2020.1814893</doi><tpages>14</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0020-7721
ispartof International journal of systems science, 2020-12, Vol.51 (16), p.3285-3298
issn 0020-7721
1464-5319
language eng
recordid cdi_crossref_primary_10_1080_00207721_2020_1814893
source Taylor and Francis Science and Technology Collection
subjects decomposition
iterative identification
Iterative methods
maximum likelihood
Multivariable system
Parameter estimation
Parameter identification
title Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T16%3A54%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Maximum%20likelihood%20iterative%20identification%20approaches%20for%20multivariable%20equation-error%20moving%20average%20systems&rft.jtitle=International%20journal%20of%20systems%20science&rft.au=Xia,%20Huafeng&rft.date=2020-12-09&rft.volume=51&rft.issue=16&rft.spage=3285&rft.epage=3298&rft.pages=3285-3298&rft.issn=0020-7721&rft.eissn=1464-5319&rft_id=info:doi/10.1080/00207721.2020.1814893&rft_dat=%3Cproquest_cross%3E2476106901%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c338t-b379c7dfaa622a4add32d33843c9f746057def1cfd0bae8eb2b1389055050a853%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2476106901&rft_id=info:pmid/&rfr_iscdi=true