Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model
. Fractional calculus extends the scope of adaptive algorithms supporting the design of novel fractional methods that outperform standard strategies in various applications arising in applied physics and engineering. In this study, a momentum fractional least-mean-square (M-FLMS) algorithm for nonli...
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| Veröffentlicht in: | European physical journal plus 2019-08, Vol.134 (8), p.407, Article 407 |
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| creator | Chaudhary, Naveed Ishtiaq Zubair, Syed Aslam, Muhammad Saeed Raja, Muhammad Asif Zahoor Machado, J. A. Tenreiro |
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Fractional calculus extends the scope of adaptive algorithms supporting the design of novel fractional methods that outperform standard strategies in various applications arising in applied physics and engineering. In this study, a momentum fractional least-mean-square (M-FLMS) algorithm for nonlinear system identification using a first and fractional-order gradient information is proposed. The M-FLMS avoids being trapped in local minima and provides faster convergence than the standard FLMS. The convergence and complexity analysis of the M-FLMS are given along with simulation results of a benchmark nonlinear system identification problem. The M-FLMS accuracy is verified through a parameter estimation problem for a nonlinear Hammerstein structure, modeling an electrically stimulated muscle (ESM) for rehabilitation of paralyzed muscles. The proposed method is studied in detail for different levels of noise variance, fractional orders and proportion of gradients used in the current update. |
| doi_str_mv | 10.1140/epjp/i2019-12785-8 |
| format | Article |
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Fractional calculus extends the scope of adaptive algorithms supporting the design of novel fractional methods that outperform standard strategies in various applications arising in applied physics and engineering. In this study, a momentum fractional least-mean-square (M-FLMS) algorithm for nonlinear system identification using a first and fractional-order gradient information is proposed. The M-FLMS avoids being trapped in local minima and provides faster convergence than the standard FLMS. The convergence and complexity analysis of the M-FLMS are given along with simulation results of a benchmark nonlinear system identification problem. The M-FLMS accuracy is verified through a parameter estimation problem for a nonlinear Hammerstein structure, modeling an electrically stimulated muscle (ESM) for rehabilitation of paralyzed muscles. The proposed method is studied in detail for different levels of noise variance, fractional orders and proportion of gradients used in the current update.</description><identifier>ISSN: 2190-5444</identifier><identifier>EISSN: 2190-5444</identifier><identifier>DOI: 10.1140/epjp/i2019-12785-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Adaptive algorithms ; Algorithms ; Applied and Technical Physics ; Applied physics ; Atomic ; Calculus ; Complex Systems ; Computer engineering ; Condensed Matter Physics ; Convergence ; Electrical engineering ; Fractional calculus ; Mathematical and Computational Physics ; Molecular ; Momentum ; Muscles ; Noise levels ; Nonlinear systems ; Optical and Plasma Physics ; Parameter estimation ; Parameter identification ; Physics ; Physics and Astronomy ; Regular Article ; System identification ; Theoretical ; Variables</subject><ispartof>European physical journal plus, 2019-08, Vol.134 (8), p.407, Article 407</ispartof><rights>Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-b66ab784add2d46ca51897fb320c7e896dcdf2f90a536919c12b07ec518a38d03</citedby><cites>FETCH-LOGICAL-c319t-b66ab784add2d46ca51897fb320c7e896dcdf2f90a536919c12b07ec518a38d03</cites><orcidid>0000-0001-6219-4910</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1140/epjp/i2019-12785-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2920359853?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Chaudhary, Naveed Ishtiaq</creatorcontrib><creatorcontrib>Zubair, Syed</creatorcontrib><creatorcontrib>Aslam, Muhammad Saeed</creatorcontrib><creatorcontrib>Raja, Muhammad Asif Zahoor</creatorcontrib><creatorcontrib>Machado, J. A. Tenreiro</creatorcontrib><title>Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model</title><title>European physical journal plus</title><addtitle>Eur. Phys. J. Plus</addtitle><description>.
Fractional calculus extends the scope of adaptive algorithms supporting the design of novel fractional methods that outperform standard strategies in various applications arising in applied physics and engineering. In this study, a momentum fractional least-mean-square (M-FLMS) algorithm for nonlinear system identification using a first and fractional-order gradient information is proposed. The M-FLMS avoids being trapped in local minima and provides faster convergence than the standard FLMS. The convergence and complexity analysis of the M-FLMS are given along with simulation results of a benchmark nonlinear system identification problem. The M-FLMS accuracy is verified through a parameter estimation problem for a nonlinear Hammerstein structure, modeling an electrically stimulated muscle (ESM) for rehabilitation of paralyzed muscles. The proposed method is studied in detail for different levels of noise variance, fractional orders and proportion of gradients used in the current update.</description><subject>Accuracy</subject><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Applied and Technical Physics</subject><subject>Applied physics</subject><subject>Atomic</subject><subject>Calculus</subject><subject>Complex Systems</subject><subject>Computer engineering</subject><subject>Condensed Matter Physics</subject><subject>Convergence</subject><subject>Electrical engineering</subject><subject>Fractional calculus</subject><subject>Mathematical and Computational Physics</subject><subject>Molecular</subject><subject>Momentum</subject><subject>Muscles</subject><subject>Noise levels</subject><subject>Nonlinear systems</subject><subject>Optical and Plasma Physics</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><subject>Physics</subject><subject>Physics and Astronomy</subject><subject>Regular Article</subject><subject>System identification</subject><subject>Theoretical</subject><subject>Variables</subject><issn>2190-5444</issn><issn>2190-5444</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kM9uGyEQxldVKtVK_AI9IeW8MbDsH46Rm9SVXOXQ9owwDC4WLFtgFfkV8tTBdqP2lLnM6NP3m9F8VfWZ4DtCGF7BdJhWlmLCa0L7oa2HD9WCEo7rljF29d_8qVqmdMClGCeMs0X18gWS3Y8oGOSDhzHPHpkoVbZhlA5tv_9AJkS0kd5DTBnsiMYwOjuCjCgdi-KR1YWzxip5otCzzb-RnCb3JuSAwIHKsQjOHVHK1s9OZtDIz0k5KKc1uJvqo5EuwfJvv65-PT78XG_q7dPXb-v7ba0awnO96zq56wcmtaaadUq2ZOC92TUUqx4G3mmlDTUcy7bpOOGK0B3uQRWbbAaNm-vq9rJ3iuHPDCmLQ5hj-TYJyiluWj60TXHRi0vFkFIEI6ZovYxHQbA4xS5OsYtz7OIcuxgK1FygVMzjHuK_1e9Qr8Jji1E</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Chaudhary, Naveed Ishtiaq</creator><creator>Zubair, Syed</creator><creator>Aslam, Muhammad Saeed</creator><creator>Raja, Muhammad Asif Zahoor</creator><creator>Machado, J. 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Fractional calculus extends the scope of adaptive algorithms supporting the design of novel fractional methods that outperform standard strategies in various applications arising in applied physics and engineering. In this study, a momentum fractional least-mean-square (M-FLMS) algorithm for nonlinear system identification using a first and fractional-order gradient information is proposed. The M-FLMS avoids being trapped in local minima and provides faster convergence than the standard FLMS. The convergence and complexity analysis of the M-FLMS are given along with simulation results of a benchmark nonlinear system identification problem. The M-FLMS accuracy is verified through a parameter estimation problem for a nonlinear Hammerstein structure, modeling an electrically stimulated muscle (ESM) for rehabilitation of paralyzed muscles. The proposed method is studied in detail for different levels of noise variance, fractional orders and proportion of gradients used in the current update.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1140/epjp/i2019-12785-8</doi><orcidid>https://orcid.org/0000-0001-6219-4910</orcidid></addata></record> |
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| subjects | Accuracy Adaptive algorithms Algorithms Applied and Technical Physics Applied physics Atomic Calculus Complex Systems Computer engineering Condensed Matter Physics Convergence Electrical engineering Fractional calculus Mathematical and Computational Physics Molecular Momentum Muscles Noise levels Nonlinear systems Optical and Plasma Physics Parameter estimation Parameter identification Physics Physics and Astronomy Regular Article System identification Theoretical Variables |
| title | Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model |
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