In this paper, particle swarm optimization (PSO) algorithm is used to optimize the parameters of kernel function in support vector regression model (SVR) to analyze and predict deformation monitoring data. The experimental results show that, compared with the results of BP neural network prediction, this method has higher prediction accuracy, better global optimization and higher operation efficiency, which is an effective method for deformation monitoring data prediction.


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    Titel :

    Research on Intelligent Deformation Monitoring Data Prediction through Particle Swarm Optimization and Support Vector Regression Model


    Beteiligte:
    Yan, Xiaohong (Autor:in) / Zhou, Jingwen (Autor:in) / Rong, Yanxiang (Autor:in) / Li, Xumin (Autor:in)


    Erscheinungsdatum :

    12.10.2022


    Format / Umfang :

    1259850 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch