Fluctuation analysis of stochastic gradient identification of polynomial Wiener systems
This correspondence presents analytical results and Monte Carlo simulations for the fluctuation behavior of a stochastic gradient adaptive identification scheme. This scheme identifies a polynomial Wiener system (linear FIR filter followed by a static polynomial nonlinearity) for noisy output observ...
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| Veröffentlicht in: | IEEE transactions on signal processing 2000-06, Vol.48 (6), p.1820-1825 |
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| container_title | IEEE transactions on signal processing |
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| creator | Celka, P. Bershad, N.J. Vesin, J.-M. |
| description | This correspondence presents analytical results and Monte Carlo simulations for the fluctuation behavior of a stochastic gradient adaptive identification scheme. This scheme identifies a polynomial Wiener system (linear FIR filter followed by a static polynomial nonlinearity) for noisy output observations. The analysis includes (1) bounds and a recursion for the misadjustment matrix and (2) algorithm mean square stability regions. A diagonal step-size matrix for the adaptive coefficients is introduced to speed up convergence. The theoretical predictions of the fluctuation analysis are supported by Monte Carlo simulations. |
| doi_str_mv | 10.1109/78.845945 |
| format | Article |
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This scheme identifies a polynomial Wiener system (linear FIR filter followed by a static polynomial nonlinearity) for noisy output observations. The analysis includes (1) bounds and a recursion for the misadjustment matrix and (2) algorithm mean square stability regions. A diagonal step-size matrix for the adaptive coefficients is introduced to speed up convergence. The theoretical predictions of the fluctuation analysis are supported by Monte Carlo simulations.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/78.845945</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Computer simulation ; Convergence ; Covariance matrix ; Finite impulse response filter ; Fluctuation ; Fluctuations ; Mathematical analysis ; Mean square values ; Monte Carlo methods ; Polynomials ; Recursion ; Signal processing algorithms ; Stability analysis ; Stochastic processes ; Stochastic resonance ; Stochastic systems ; Stochasticity</subject><ispartof>IEEE transactions on signal processing, 2000-06, Vol.48 (6), p.1820-1825</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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This scheme identifies a polynomial Wiener system (linear FIR filter followed by a static polynomial nonlinearity) for noisy output observations. The analysis includes (1) bounds and a recursion for the misadjustment matrix and (2) algorithm mean square stability regions. A diagonal step-size matrix for the adaptive coefficients is introduced to speed up convergence. The theoretical predictions of the fluctuation analysis are supported by Monte Carlo simulations.</description><subject>Computer simulation</subject><subject>Convergence</subject><subject>Covariance matrix</subject><subject>Finite impulse response filter</subject><subject>Fluctuation</subject><subject>Fluctuations</subject><subject>Mathematical analysis</subject><subject>Mean square values</subject><subject>Monte Carlo methods</subject><subject>Polynomials</subject><subject>Recursion</subject><subject>Signal processing algorithms</subject><subject>Stability analysis</subject><subject>Stochastic processes</subject><subject>Stochastic resonance</subject><subject>Stochastic systems</subject><subject>Stochasticity</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0TtPwzAQAOAIgUQpDKxMEQOIIcXvx4gqCkiVWEBls2zHAVdJXOJkyL_HKBUDAyx3p7tPN9xl2TkECwiBvOViIQiVhB5kMygJLADh7DDVgOKCCv52nJ3EuAUAEiLZLNus6sH2g-59aHPd6nqMPuahymMf7IeOvbf5e6dL79o-92WKvvJ24kntQj22ofG6zjeJuC6PY-xdE0-zo0rX0Z3t8zx7Xd2_LB-L9fPD0_JuXVjMeF9Y5pBEGDFQEglKbgDRVkjiBDdGG2qcLTECqUUhAcZUFBuY-hUqCUFO4nl2Pe3ddeFzcLFXjY_W1bVuXRiikpAwzCQGSV79KZFAjEGM_4ecAok5T_DyF9yGoUs3jEoIIoRMKqGbCdkuxNi5Su063-huVBCo75cpLtT0smQvJuudcz9uP_wCmjyRwg</recordid><startdate>20000601</startdate><enddate>20000601</enddate><creator>Celka, P.</creator><creator>Bershad, N.J.</creator><creator>Vesin, J.-M.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20000601</creationdate><title>Fluctuation analysis of stochastic gradient identification of polynomial Wiener systems</title><author>Celka, P. ; Bershad, N.J. ; Vesin, J.-M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-c6e2923260d490d7b04ac894e87bbab5becd320c895140bbf53b1ab5f2d442e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Computer simulation</topic><topic>Convergence</topic><topic>Covariance matrix</topic><topic>Finite impulse response filter</topic><topic>Fluctuation</topic><topic>Fluctuations</topic><topic>Mathematical analysis</topic><topic>Mean square values</topic><topic>Monte Carlo methods</topic><topic>Polynomials</topic><topic>Recursion</topic><topic>Signal processing algorithms</topic><topic>Stability analysis</topic><topic>Stochastic processes</topic><topic>Stochastic resonance</topic><topic>Stochastic systems</topic><topic>Stochasticity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Celka, P.</creatorcontrib><creatorcontrib>Bershad, N.J.</creatorcontrib><creatorcontrib>Vesin, J.-M.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications 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><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Celka, P.</au><au>Bershad, N.J.</au><au>Vesin, J.-M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fluctuation analysis of stochastic gradient identification of polynomial Wiener systems</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2000-06-01</date><risdate>2000</risdate><volume>48</volume><issue>6</issue><spage>1820</spage><epage>1825</epage><pages>1820-1825</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>This correspondence presents analytical results and Monte Carlo simulations for the fluctuation behavior of a stochastic gradient adaptive identification scheme. This scheme identifies a polynomial Wiener system (linear FIR filter followed by a static polynomial nonlinearity) for noisy output observations. The analysis includes (1) bounds and a recursion for the misadjustment matrix and (2) algorithm mean square stability regions. A diagonal step-size matrix for the adaptive coefficients is introduced to speed up convergence. The theoretical predictions of the fluctuation analysis are supported by Monte Carlo simulations.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/78.845945</doi><tpages>6</tpages></addata></record> |
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| ispartof | IEEE transactions on signal processing, 2000-06, Vol.48 (6), p.1820-1825 |
| issn | 1053-587X 1941-0476 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_28266133 |
| source | IEEE Electronic Library (IEL) |
| subjects | Computer simulation Convergence Covariance matrix Finite impulse response filter Fluctuation Fluctuations Mathematical analysis Mean square values Monte Carlo methods Polynomials Recursion Signal processing algorithms Stability analysis Stochastic processes Stochastic resonance Stochastic systems Stochasticity |
| title | Fluctuation analysis of stochastic gradient identification of polynomial Wiener systems |
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