A global convergent outlier robust adaptive predictor for MIMO Hammerstein models
Summary The paper considers the outlier‐robust recursive stochastic approximation algorithm for adaptive prediction of multiple‐input multiple‐output (MIMO) Hammerstein model with a static nonlinear block in polynomial form and a linear block is output error (OE) model. It is assumed that there is a...
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| Veröffentlicht in: | International journal of robust and nonlinear control 2017-11, Vol.27 (16), p.3350-3371 |
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| container_title | International journal of robust and nonlinear control |
| container_volume | 27 |
| creator | Filipovic, Vojislav Z. |
| description | Summary
The paper considers the outlier‐robust recursive stochastic approximation algorithm for adaptive prediction of multiple‐input multiple‐output (MIMO) Hammerstein model with a static nonlinear block in polynomial form and a linear block is output error (OE) model. It is assumed that there is a priori information about a distribution class to which a real disturbance belongs. Within the framework of these assumptions, the main contributions of this paper are: (i) for MIMO Hammerstein OE model, the stochastic approximation algorithm, based on robust statistics (in the sense of Huber), is derived; (ii) scalar gain of algorithm is exactly determined using the Laplace function; and (iii) a global convergence of robust adaptive predictor is proved. The proof is based on martingale theory and generalized strictly positive real conditions. Practical behavior of algorithm was illustrated by simulations. Copyright © 2016 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/rnc.3705 |
| format | Article |
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The paper considers the outlier‐robust recursive stochastic approximation algorithm for adaptive prediction of multiple‐input multiple‐output (MIMO) Hammerstein model with a static nonlinear block in polynomial form and a linear block is output error (OE) model. It is assumed that there is a priori information about a distribution class to which a real disturbance belongs. Within the framework of these assumptions, the main contributions of this paper are: (i) for MIMO Hammerstein OE model, the stochastic approximation algorithm, based on robust statistics (in the sense of Huber), is derived; (ii) scalar gain of algorithm is exactly determined using the Laplace function; and (iii) a global convergence of robust adaptive predictor is proved. The proof is based on martingale theory and generalized strictly positive real conditions. Practical behavior of algorithm was illustrated by simulations. Copyright © 2016 John Wiley & Sons, Ltd.</description><identifier>ISSN: 1049-8923</identifier><identifier>EISSN: 1099-1239</identifier><identifier>DOI: 10.1002/rnc.3705</identifier><language>eng</language><publisher>Bognor Regis: Wiley Subscription Services, Inc</publisher><subject>Adaptive algorithms ; Algorithms ; Approximation ; Computer simulation ; Convergence ; global convergence ; Mathematical analysis ; Mathematical models ; MIMO Hammerstein model ; outliers ; prediction ; Robustness ; stochastic approximation</subject><ispartof>International journal of robust and nonlinear control, 2017-11, Vol.27 (16), p.3350-3371</ispartof><rights>Copyright © 2016 John Wiley & Sons, Ltd.</rights><rights>Copyright © 2017 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3305-20b9611b8802c061c1142ec1d5446c7d1eda136049428876b73c305dc4ae87c3</citedby><cites>FETCH-LOGICAL-c3305-20b9611b8802c061c1142ec1d5446c7d1eda136049428876b73c305dc4ae87c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Frnc.3705$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Frnc.3705$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids></links><search><creatorcontrib>Filipovic, Vojislav Z.</creatorcontrib><title>A global convergent outlier robust adaptive predictor for MIMO Hammerstein models</title><title>International journal of robust and nonlinear control</title><description>Summary
The paper considers the outlier‐robust recursive stochastic approximation algorithm for adaptive prediction of multiple‐input multiple‐output (MIMO) Hammerstein model with a static nonlinear block in polynomial form and a linear block is output error (OE) model. It is assumed that there is a priori information about a distribution class to which a real disturbance belongs. Within the framework of these assumptions, the main contributions of this paper are: (i) for MIMO Hammerstein OE model, the stochastic approximation algorithm, based on robust statistics (in the sense of Huber), is derived; (ii) scalar gain of algorithm is exactly determined using the Laplace function; and (iii) a global convergence of robust adaptive predictor is proved. The proof is based on martingale theory and generalized strictly positive real conditions. Practical behavior of algorithm was illustrated by simulations. Copyright © 2016 John Wiley & Sons, Ltd.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Approximation</subject><subject>Computer simulation</subject><subject>Convergence</subject><subject>global convergence</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>MIMO Hammerstein model</subject><subject>outliers</subject><subject>prediction</subject><subject>Robustness</subject><subject>stochastic approximation</subject><issn>1049-8923</issn><issn>1099-1239</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp1kEtLAzEQgIMoWKvgTwh48bI1k-wrx1LUFlqL0nvIZqdly-5mTXYr_fdNrVcPwwzMNw8-Qh6BTYAx_uJaMxEZS67ICJiUEXAhr891LKNccnFL7rzfMxZ6PB6Rzynd1bbQNTW2PaDbYdtTO_R1hY46Wwy-p7rUXV8dkHYOy8r01tFtiNVitaZz3TTofI9VSxtbYu3vyc1W1x4f_vKYbN5eN7N5tFy_L2bTZWSEYEnEWSFTgCLPGTcsBQMQczRQJnGcmqwELDWINLwd8zzP0iITJsyVJtaYZ0aMydNlbefs94C-V3s7uDZcVCATYABSQqCeL5Rx1nuHW9W5qtHuqICpsy8VfKmzr4BGF_SnqvH4L6e-Pma__Ak8Mmq5</recordid><startdate>20171110</startdate><enddate>20171110</enddate><creator>Filipovic, Vojislav Z.</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20171110</creationdate><title>A global convergent outlier robust adaptive predictor for MIMO Hammerstein models</title><author>Filipovic, Vojislav Z.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3305-20b9611b8802c061c1142ec1d5446c7d1eda136049428876b73c305dc4ae87c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Approximation</topic><topic>Computer simulation</topic><topic>Convergence</topic><topic>global convergence</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>MIMO Hammerstein model</topic><topic>outliers</topic><topic>prediction</topic><topic>Robustness</topic><topic>stochastic approximation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Filipovic, Vojislav Z.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering 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 robust and nonlinear control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Filipovic, Vojislav Z.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A global convergent outlier robust adaptive predictor for MIMO Hammerstein models</atitle><jtitle>International journal of robust and nonlinear control</jtitle><date>2017-11-10</date><risdate>2017</risdate><volume>27</volume><issue>16</issue><spage>3350</spage><epage>3371</epage><pages>3350-3371</pages><issn>1049-8923</issn><eissn>1099-1239</eissn><abstract>Summary
The paper considers the outlier‐robust recursive stochastic approximation algorithm for adaptive prediction of multiple‐input multiple‐output (MIMO) Hammerstein model with a static nonlinear block in polynomial form and a linear block is output error (OE) model. It is assumed that there is a priori information about a distribution class to which a real disturbance belongs. Within the framework of these assumptions, the main contributions of this paper are: (i) for MIMO Hammerstein OE model, the stochastic approximation algorithm, based on robust statistics (in the sense of Huber), is derived; (ii) scalar gain of algorithm is exactly determined using the Laplace function; and (iii) a global convergence of robust adaptive predictor is proved. The proof is based on martingale theory and generalized strictly positive real conditions. Practical behavior of algorithm was illustrated by simulations. Copyright © 2016 John Wiley & Sons, Ltd.</abstract><cop>Bognor Regis</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/rnc.3705</doi><tpages>22</tpages></addata></record> |
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| issn | 1049-8923 1099-1239 |
| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Adaptive algorithms Algorithms Approximation Computer simulation Convergence global convergence Mathematical analysis Mathematical models MIMO Hammerstein model outliers prediction Robustness stochastic approximation |
| title | A global convergent outlier robust adaptive predictor for MIMO Hammerstein models |
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