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Nonlinear Identification With Local Model Networks Using GTLS Techniques and Equality Constraints
Local model networks approximate a nonlinear system through multiple local models fitted within a partition space. The main advantage of this approach is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process. This paper exte...
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| Published in: | IEEE transaction on neural networks and learning systems 2011-09, Vol.22 (9), p.1406-1418 |
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| cites | cdi_FETCH-LOGICAL-c440t-7364f806018fe0ddf26b3e5673ece6ad4118567d8fa28a40bfdb078f7efa04173 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Hametner, C. Jakubek, S. |
| description | Local model networks approximate a nonlinear system through multiple local models fitted within a partition space. The main advantage of this approach is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process. This paper extends these concepts by the integration of quantitative process knowledge into the identification procedure. Quantitative knowledge describes explicit dependences between inputs and outputs and is integrated in the parameter estimation process by means of equality constraints. For this purpose, a constrained generalized total least squares algorithm for local parameter estimation is presented. Furthermore, the problem of proper integration of constraints in the partitioning process is treated where an expectation-maximization procedure is combined with constrained parameter estimation. The benefits and the applicability of the proposed concepts are demonstrated by means of two illustrative examples and a practical application using real measurement data. |
| doi_str_mv | 10.1109/TNN.2011.2159309 |
| format | article |
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The main advantage of this approach is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process. This paper extends these concepts by the integration of quantitative process knowledge into the identification procedure. Quantitative knowledge describes explicit dependences between inputs and outputs and is integrated in the parameter estimation process by means of equality constraints. For this purpose, a constrained generalized total least squares algorithm for local parameter estimation is presented. Furthermore, the problem of proper integration of constraints in the partitioning process is treated where an expectation-maximization procedure is combined with constrained parameter estimation. 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Data processing ; Models, Theoretical ; Networks ; Neural networks ; Noise ; Noise measurement ; Nonlinear Dynamics ; nonlinear system identification ; Nonlinearity ; Optimization ; Parameter estimation ; Partitioning algorithms ; Software ; Studies</subject><ispartof>IEEE transaction on neural networks and learning systems, 2011-09, Vol.22 (9), p.1406-1418</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The main advantage of this approach is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process. This paper extends these concepts by the integration of quantitative process knowledge into the identification procedure. Quantitative knowledge describes explicit dependences between inputs and outputs and is integrated in the parameter estimation process by means of equality constraints. For this purpose, a constrained generalized total least squares algorithm for local parameter estimation is presented. Furthermore, the problem of proper integration of constraints in the partitioning process is treated where an expectation-maximization procedure is combined with constrained parameter estimation. 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Data processing</subject><subject>Models, Theoretical</subject><subject>Networks</subject><subject>Neural networks</subject><subject>Noise</subject><subject>Noise measurement</subject><subject>Nonlinear Dynamics</subject><subject>nonlinear system identification</subject><subject>Nonlinearity</subject><subject>Optimization</subject><subject>Parameter estimation</subject><subject>Partitioning algorithms</subject><subject>Software</subject><subject>Studies</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqFkdFrFDEQhxdRbK2-C4IEQfRlz5ndZJM8lqOthfN88IqPS253YlP3kjbZRfrfN8udFXzQpyTkm9_M8BXFa4QFIuhPm_V6UQHiokKha9BPimPUHEsAXT_Nd-Ci1FUlj4oXKd0AIBfQPC-OKpRKoVLHhVkHPzhPJrLLnvzorOvM6IJn3914zVahMwP7Enoa2JrGXyH-TOwqOf-DXWxW39iGumvv7iZKzPiend1NZnDjPVsGn8ZonB_Ty-KZNUOiV4fzpLg6P9ssP5errxeXy9NV2XEOYynrhlsFDaCyBH1vq2Zbk2hkTR01pueIKr96ZU2lDIet7bcglZVkDXCU9UnxYZ97G8M80NjuXOpoGIynMKVWo9Y1b_T_SaWkACH5TH78J4mQLaDIQ2f03V_oTZiizxvnzhnIe4kMwR7qYkgpkm1vo9uZeJ-T2tlom422s9H2YDSXvD3kTtsd9Y8FvxVm4P0BMCnLstH4zqU_HBd1JfXc-82ec0T0-C20UA1i_QCvo7Ak</recordid><startdate>20110901</startdate><enddate>20110901</enddate><creator>Hametner, C.</creator><creator>Jakubek, S.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Data processing</topic><topic>Models, Theoretical</topic><topic>Networks</topic><topic>Neural networks</topic><topic>Noise</topic><topic>Noise measurement</topic><topic>Nonlinear Dynamics</topic><topic>nonlinear system identification</topic><topic>Nonlinearity</topic><topic>Optimization</topic><topic>Parameter estimation</topic><topic>Partitioning algorithms</topic><topic>Software</topic><topic>Studies</topic><toplevel>online_resources</toplevel><creatorcontrib>Hametner, C.</creatorcontrib><creatorcontrib>Jakubek, S.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hametner, C.</au><au>Jakubek, S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonlinear Identification With Local Model Networks Using GTLS Techniques and Equality Constraints</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>2011-09-01</date><risdate>2011</risdate><volume>22</volume><issue>9</issue><spage>1406</spage><epage>1418</epage><pages>1406-1418</pages><issn>1045-9227</issn><issn>2162-237X</issn><eissn>1941-0093</eissn><eissn>2162-2388</eissn><coden>ITNNEP</coden><abstract>Local model networks approximate a nonlinear system through multiple local models fitted within a partition space. 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| source | IEEE Electronic Library (IEL) Journals |
| subjects | Algorithms Applied sciences Approximation Artificial Intelligence Computer science control theory systems Constraints Eigenvalues and eigenfunctions Equality constraints Exact sciences and technology generalized total least squares Humans Image reconstruction Information systems. Data bases Least squares method Least-Squares Analysis local model network Mathematical models Memory organisation. Data processing Models, Theoretical Networks Neural networks Noise Noise measurement Nonlinear Dynamics nonlinear system identification Nonlinearity Optimization Parameter estimation Partitioning algorithms Software Studies |
| title | Nonlinear Identification With Local Model Networks Using GTLS Techniques and Equality Constraints |
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