Analysis of stochastic gradient identification of polynomial nonlinear systems with memory
This paper presents analytical, numerical and experimental results for a stochastic gradient adaptive scheme which identifies a polynomial-type nonlinear system with memory for noisy output observations. The analysis includes the computation of the stationary points, the mean square error surface, a...
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| creator | Celka, P. Bershad, N.J. Vesin, J.M. |
| description | This paper presents analytical, numerical and experimental results for a stochastic gradient adaptive scheme which identifies a polynomial-type nonlinear system with memory for noisy output observations. The analysis includes the computation of the stationary points, the mean square error surface, and the mean behaviour of the algorithm for Gaussian data. Monte Carlo simulations confirm the theoretical predictions which show a small sensitivity to the observation noise. |
| doi_str_mv | 10.1109/ICASSP.1999.756216 |
| format | Conference Proceeding |
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The analysis includes the computation of the stationary points, the mean square error surface, and the mean behaviour of the algorithm for Gaussian data. Monte Carlo simulations confirm the theoretical predictions which show a small sensitivity to the observation noise.</description><identifier>ISSN: 1520-6149</identifier><identifier>ISBN: 0780350413</identifier><identifier>ISBN: 9780780350410</identifier><identifier>EISSN: 2379-190X</identifier><identifier>DOI: 10.1109/ICASSP.1999.756216</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Least squares approximation ; Mean square error methods ; Nonlinear filters ; Nonlinear systems ; Polynomials ; Signal analysis ; Signal processing algorithms ; Signal to noise ratio ; Stochastic systems</subject><ispartof>1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. 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No.99CH36258)</title><addtitle>ICASSP</addtitle><description>This paper presents analytical, numerical and experimental results for a stochastic gradient adaptive scheme which identifies a polynomial-type nonlinear system with memory for noisy output observations. The analysis includes the computation of the stationary points, the mean square error surface, and the mean behaviour of the algorithm for Gaussian data. Monte Carlo simulations confirm the theoretical predictions which show a small sensitivity to the observation noise.</description><subject>Algorithm design and analysis</subject><subject>Least squares approximation</subject><subject>Mean square error methods</subject><subject>Nonlinear filters</subject><subject>Nonlinear systems</subject><subject>Polynomials</subject><subject>Signal analysis</subject><subject>Signal processing algorithms</subject><subject>Signal to noise ratio</subject><subject>Stochastic systems</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>0780350413</isbn><isbn>9780780350410</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1999</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkN1KAzEUhIM_YK19gV7lBbae_O1uLktRKxQUqiDelNPdxB7ZTcomIPv2VupczNx8DMMwNhewEALs_fNqud2-LoS1dlGZUorygk2kqmwhLHxcsluoalAGtFBXbCKMhKIU2t6wWUrfcJI2Bio1YZ_LgN2YKPHoecqxOWDK1PCvAVtyIXNqT06eGswUwx91jN0YYk_Y8RBDR8HhwNOYsusT_6F84L3r4zDesWuPXXKz_5yy98eHt9W62Lw8nfZvCpKgcqGhVtpqNA2AcHVpoUUBBuReIyglEFzZYK2l8drXpQen2j14C7pFRKnUlM3PveSc2x0H6nEYd-dX1C9Z3lYQ</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Celka, P.</creator><creator>Bershad, N.J.</creator><creator>Vesin, J.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>1999</creationdate><title>Analysis of stochastic gradient identification of polynomial nonlinear systems with memory</title><author>Celka, P. ; Bershad, N.J. ; Vesin, J.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-4083494a5c001e8690da10502b4a0331a0e6ca8425f4f86f0e3db0f904daaa233</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Algorithm design and analysis</topic><topic>Least squares approximation</topic><topic>Mean square error methods</topic><topic>Nonlinear filters</topic><topic>Nonlinear systems</topic><topic>Polynomials</topic><topic>Signal analysis</topic><topic>Signal processing algorithms</topic><topic>Signal to noise ratio</topic><topic>Stochastic systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Celka, P.</creatorcontrib><creatorcontrib>Bershad, N.J.</creatorcontrib><creatorcontrib>Vesin, J.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></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>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Analysis of stochastic gradient identification of polynomial nonlinear systems with memory</atitle><btitle>1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)</btitle><stitle>ICASSP</stitle><date>1999</date><risdate>1999</risdate><volume>3</volume><spage>1293</spage><epage>1296 vol.3</epage><pages>1293-1296 vol.3</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>0780350413</isbn><isbn>9780780350410</isbn><abstract>This paper presents analytical, numerical and experimental results for a stochastic gradient adaptive scheme which identifies a polynomial-type nonlinear system with memory for noisy output observations. The analysis includes the computation of the stationary points, the mean square error surface, and the mean behaviour of the algorithm for Gaussian data. Monte Carlo simulations confirm the theoretical predictions which show a small sensitivity to the observation noise.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.1999.756216</doi></addata></record> |
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| identifier | ISSN: 1520-6149 |
| ispartof | 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999, Vol.3, p.1293-1296 vol.3 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_756216 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Least squares approximation Mean square error methods Nonlinear filters Nonlinear systems Polynomials Signal analysis Signal processing algorithms Signal to noise ratio Stochastic systems |
| title | Analysis of stochastic gradient identification of polynomial nonlinear systems with memory |
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