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    AuthorTitleYearJournal/ProceedingsDOI/URL
    Matthias Kahl, Andreas Kroll Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models 2020 IFAC-PapersOnLine, 21th IFAC World Congress, vol. 53, no. 2, pp. 1182-1187, Elsevier, Berlin, Germany, IFAC   
    Abstract: In this paper, the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is adressed. It is solved by reformulating the TS model in its Linear Parameter Varying (LPV) form and applying an extension of a recently proposed Regularized Least Squares Support Vector Machine (R-LSSVM) technique for LPV models. For that, a nonparametric formulation of the TS identi cation problem is proposed which uses data-dependent basis functions. By doing so, the partition of unity of the TS model is preserved and the scheduling dependencies of the model are obtained in a nonparametric manner. For the local order selection, a regularization approach is used which forces the coeffcient functions of insignifcant values of the lagged input and output towards zero.
    BibTeX:
    @inproceedings{Kahl-IFAC-2020,
     abstract = {In this paper, the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is adressed. It is solved by reformulating the TS model in its Linear Parameter Varying (LPV) form and applying an extension of a recently proposed Regularized Least Squares Support Vector Machine (R-LSSVM) technique for LPV models. For that, a nonparametric formulation of the TS identi cation problem is proposed which uses data-dependent basis functions. By doing so, the partition of unity of the TS model is preserved and the scheduling dependencies of the model are obtained in a nonparametric manner. For the local order selection, a regularization approach is used which forces the coeffcient functions of insignifcant values of the lagged input and output towards zero.},
     address = {Berlin, Germany},
     author = {Matthias Kahl and Andreas Kroll},
     booktitle = {21th IFAC World Congress},
     journal = {IFAC-PapersOnLine},
     language = {english},
     mrtnote = {peer,SFS_TS},
     number = {2},
     organization = {IFAC},
     owner = {duerrbaum},
     pages = {1182--1187},
     publisher = {Elsevier},
     timestamp = {2019.11.25},
     title = {Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno
    Models},
     volume = {53},
     year = {2020}
    }
    
    
    Matthias Kahl Zur Strukturselektion bei dynamischen lokal-affinen Multi-Modellen mittels statistischer Methoden 2018 52. Regelungstechnisches Kolloquium, Boppard, Fraunhofer IOSB, 21.-23. Februar  URL  
    BibTeX:
    @conference{Boppard2018,
     author = {Matthias Kahl},
     booktitle = {52. Regelungstechnisches Kolloquium, Boppard},
     month = {21.-23. Februar},
     mrtnote = {nopeer,FuzzyIdControl,SFS_TS},
     organization = {Fraunhofer IOSB},
     owner = {duerrbaum},
     timestamp = {2016.02.22},
     title = {Zur Strukturselektion bei dynamischen lokal-affinen Multi-Modellen mittels statistischer
    Methoden},
     url = {https://www.iosb.fraunhofer.de/?boppard},
     year = {2018}
    }
    
    
    Kahl, Matthias, Kroll, Andreas Structure Identification of Dynamical Takagi-Sugeno Fuzzy Models by Using LPV Techniques 2018 Archives of Data Science, Series A (Online First), vol. 5, no. 1, pp. A19, 17 S. online  DOI  
    BibTeX:
    @article{ECDA2018_full,
     author = {Kahl, Matthias and Kroll, Andreas},
     doi = {10.5445/KSP/1000087327/19},
     issn = {2363-9881},
     journal = {Archives of Data Science, Series A (Online First)},
     language = {english},
     mrtnote = {peer,SFS_TS},
     number = {1},
     pages = {A19, 17 S. online},
     title = {Structure Identification of Dynamical Takagi-Sugeno Fuzzy Models by Using LPV
    Techniques},
     volume = {5},
     year = {2018}
    }
    
    
    Matthias Kahl, Andreas Kroll, Robert Kästner and Manfried Sofsky Application of model selection methods for the identification of a dynamic boost pressure model 2015 Proceedings of the 17th IFAC Symposium on System Identification (SysID), pp. 829-834, Beijing, China, October 19-21  DOI  
    BibTeX:
    @inproceedings{KahlSysID2015,
     address = {Beijing, China},
     author = {Matthias Kahl and Andreas Kroll and Robert Kästner
    and Manfried Sofsky},
     booktitle = {Proceedings of the 17th IFAC Symposium on System
    Identification ({SysID})},
     doi = {doi:10.1016/j.ifacol.2015.12.232},
     language = {english},
     month = {October 19-21},
     mrtnote = {peer,DynModNL,SFS_TS},
     owner = {duerrbaum},
     pages = {829-834},
     timestamp = {2015.03.25},
     title = {Application of model selection methods for the identification of a dynamic boost pressure
    model},
     year = {2015}
    }
    
    
    Matthias Kahl, Andreas Kroll, Robert Kästner and Manfried Sofsky Zur automatisierten Auswahl signifikanter Regressoren für die Identifikation eines dynamischen Ladedruckmodells 2014 24. Workshop Computational Intelligence, pp. 33-53, Schriftenreihe des Instituts für Angewandte Informatik / Automatisierungstechnik, KIT Scientific Publishing, Dortmund, GMA-FA 5.14 "Computational Intelligence" und GI-FG "Fuzzy-Systeme und Soft-Computing", 27.-28. November  DOI  
    BibTeX:
    @inproceedings{KahlGMA2014,
     address = {Dortmund},
     author = {Matthias Kahl and Andreas Kroll and Robert Kästner
    and Manfried Sofsky},
     booktitle = {24. Workshop Computational Intelligence},
     doi = {10.5445/KSP/1000043427},
     editor = {Frank Hoffmann and Eike Hüllermeier},
     month = {27.-28. November},
     mrtnote = {nopeer,DynModNL,pke,SFS_TS},
     organization = {GMA-FA 5.14 "Computational Intelligence" und GI-FG "Fuzzy-Systeme und
    Soft-Computing"},
     owner = {kahl},
     pages = {33-53},
     publisher = {KIT Scientific Publishing},
     series = {Schriftenreihe des Instituts für Angewandte
    Informatik / Automatisierungstechnik},
     timestamp = {2014.09.23},
     title = {Zur automatisierten Auswahl signifikanter Regressoren für die Identifikation eines dynamischen Ladedruckmodells},
     year = {2014}
    }
    
    

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