An orthogonal ARMA identifier with automatic order estimation for biological modeling
In this paper an ARMA identification algorithm is developed for modeling biological time series data. The algorithm is based on Gram-Schmidt orthogonalization of automatically selected basis functions from a specified function space. The selection criterion is based on recursive testing of potential...
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| Veröffentlicht in: | Annals of biomedical engineering 1989-01, Vol.17 (6), p.571-592 |
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| container_end_page | 592 |
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| container_issue | 6 |
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| container_title | Annals of biomedical engineering |
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| creator | KORENBERG, M. J PAARMANN, L. D |
| description | In this paper an ARMA identification algorithm is developed for modeling biological time series data. The algorithm is based on Gram-Schmidt orthogonalization of automatically selected basis functions from a specified function space. The selection criterion is based on recursive testing of potential benefit to the model of candidate functions. The candidate functions, AR and MA terms, are tested in a pair-wise search direction until a least-squares criterion is satisfied, thereby estimating the order. Additive noise is considered and the basic algorithm extended to improve performance in noise. The algorithm is also extended to systems with inaccessible inputs (signal modeling). Modeling of biological data from speech is included, and indicates good performance. The algorithm is derived from earlier work on nonlinear systems identification. |
| doi_str_mv | 10.1007/BF02367464 |
| format | Article |
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J ; PAARMANN, L. D</creator><creatorcontrib>KORENBERG, M. J ; PAARMANN, L. D</creatorcontrib><description>In this paper an ARMA identification algorithm is developed for modeling biological time series data. The algorithm is based on Gram-Schmidt orthogonalization of automatically selected basis functions from a specified function space. The selection criterion is based on recursive testing of potential benefit to the model of candidate functions. The candidate functions, AR and MA terms, are tested in a pair-wise search direction until a least-squares criterion is satisfied, thereby estimating the order. Additive noise is considered and the basic algorithm extended to improve performance in noise. The algorithm is also extended to systems with inaccessible inputs (signal modeling). Modeling of biological data from speech is included, and indicates good performance. The algorithm is derived from earlier work on nonlinear systems identification.</description><identifier>ISSN: 0090-6964</identifier><identifier>EISSN: 1573-9686</identifier><identifier>DOI: 10.1007/BF02367464</identifier><identifier>PMID: 2589693</identifier><identifier>CODEN: ABMECF</identifier><language>eng</language><publisher>New York, NY: Springer</publisher><subject>Algorithms ; Biological and medical sciences ; Computer Simulation ; Computer Systems ; Computerized, statistical medical data processing and models in biomedicine ; Humans ; Least-Squares Analysis ; Medical sciences ; Models and simulation ; Models, Biological ; Signal Processing, Computer-Assisted ; Software</subject><ispartof>Annals of biomedical engineering, 1989-01, Vol.17 (6), p.571-592</ispartof><rights>1990 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c311t-fce0e1744c04a945a2b174da59bfee83f2023b7e204e72c62efc91932851efd73</citedby><cites>FETCH-LOGICAL-c311t-fce0e1744c04a945a2b174da59bfee83f2023b7e204e72c62efc91932851efd73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=6676961$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/2589693$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>KORENBERG, M. 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Modeling of biological data from speech is included, and indicates good performance. The algorithm is derived from earlier work on nonlinear systems identification.</description><subject>Algorithms</subject><subject>Biological and medical sciences</subject><subject>Computer Simulation</subject><subject>Computer Systems</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Humans</subject><subject>Least-Squares Analysis</subject><subject>Medical sciences</subject><subject>Models and simulation</subject><subject>Models, Biological</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Software</subject><issn>0090-6964</issn><issn>1573-9686</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1989</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpFkMFLwzAUxoMoc04v3oUexINQTZo0aY5zOBUmgrhzSdOXLdI2mrSI_70ZK_P0eO_7vcf7PoQuCb4jGIv7hyXOKBeMsyM0JbmgqeQFP0ZTjCVOueTsFJ2F8IkxIQXNJ2iS5YXkkk7Ret4lzvdbt3GdapL5--s8sTV0vTUWfPJj-22iht61qrc6knUcQujtrnddYpxPKusat7E6rreuhsZ2m3N0YlQT4GKsM7RePn4sntPV29PLYr5KNSWkT40GDEQwpjFTkuUqq2JXq1xWBqCgJou-KgEZZiAyzTMwWhJJsyInYGpBZ-hmf_fLu-8h_lW2NmhoGtWBG0IpJM15QVgEb_eg9i4ED6b88tGD_y0JLncZlv8ZRvhqvDpULdQHdAwt6tejrkJ0bbzqtA0HjHMRIyf0D8V2eMQ</recordid><startdate>19890101</startdate><enddate>19890101</enddate><creator>KORENBERG, M. 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D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An orthogonal ARMA identifier with automatic order estimation for biological modeling</atitle><jtitle>Annals of biomedical engineering</jtitle><addtitle>Ann Biomed Eng</addtitle><date>1989-01-01</date><risdate>1989</risdate><volume>17</volume><issue>6</issue><spage>571</spage><epage>592</epage><pages>571-592</pages><issn>0090-6964</issn><eissn>1573-9686</eissn><coden>ABMECF</coden><abstract>In this paper an ARMA identification algorithm is developed for modeling biological time series data. The algorithm is based on Gram-Schmidt orthogonalization of automatically selected basis functions from a specified function space. The selection criterion is based on recursive testing of potential benefit to the model of candidate functions. The candidate functions, AR and MA terms, are tested in a pair-wise search direction until a least-squares criterion is satisfied, thereby estimating the order. Additive noise is considered and the basic algorithm extended to improve performance in noise. The algorithm is also extended to systems with inaccessible inputs (signal modeling). Modeling of biological data from speech is included, and indicates good performance. The algorithm is derived from earlier work on nonlinear systems identification.</abstract><cop>New York, NY</cop><pub>Springer</pub><pmid>2589693</pmid><doi>10.1007/BF02367464</doi><tpages>22</tpages></addata></record> |
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| ispartof | Annals of biomedical engineering, 1989-01, Vol.17 (6), p.571-592 |
| issn | 0090-6964 1573-9686 |
| language | eng |
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| source | MEDLINE; SpringerLink Journals |
| subjects | Algorithms Biological and medical sciences Computer Simulation Computer Systems Computerized, statistical medical data processing and models in biomedicine Humans Least-Squares Analysis Medical sciences Models and simulation Models, Biological Signal Processing, Computer-Assisted Software |
| title | An orthogonal ARMA identifier with automatic order estimation for biological modeling |
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