Accurately determining system order plays a vital role in system identification directly related to the accuracy of identification results, especially for nonlinear system identification. Due to the need for human subjective judgment, the traditional sequence determination method easily causes uncertainty in the results; and the phenomenon of the virtual mode or omission occurs. An automatic nonlinear subspace identification method is proposed to address the aforementioned problems. When the eigenvalue decomposition of the constructed Hankel matrix is performed, the calculation range of the modal order of the system is estimated. The similarity coefficient and distance function are introduced to cluster the identified modal results, the poles of the false modes are removed to obtain the cluster stabilization diagram, and the best order of the system is received. Then, the modal parameters and nonlinear coefficients are obtained. Simulation examples are carried out to verify the effectiveness and robustness of the proposed method. An experimental study is carried out on a multilayer building with nonlinear characteristics. Compared with the traditional stabilization graph, the accuracy of the automatic order determination proposed in this paper is proven.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Automatic Nonlinear Subspace Identification Using Clustering Judgment Based on Similarity Filtering


    Beteiligte:
    Zhu, Rui (Autor:in) / Jiang, Dong (Autor:in) / Marchesiello, Stefano (Autor:in) / Anastasio, Dario (Autor:in) / Zhang, Dahai (Autor:in) / Fei, Qingguo (Autor:in)

    Erschienen in:

    AIAA Journal ; 61 , 6 ; 2666-2674


    Erscheinungsdatum :

    01.06.2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval

    Fu, Y. / Li, Z. / Huang, T. S. et al. | British Library Online Contents | 2008


    Reweighted sparse subspace clustering

    Xu, J. / Xu, K. / Chen, K. et al. | British Library Online Contents | 2015


    Bayesian Model Selection in Nonlinear Subspace Identification

    Zhu, Rui / Fei, Qingguo / Jiang, Dong et al. | AIAA | 2022


    Nonlinear Model Identification of a Ship Using Recursive Subspace Methods

    Xia, Guoqing / Zhao, Ang / Chen, Xinghua et al. | British Library Conference Proceedings | 2015


    A Robust Subspace Clustering Algorithm

    Peng, L. / Zhang, J. | British Library Online Contents | 2011