• DocumentCode
    2649893
  • Title

    The temperature system identification of the PVC stripper tower top based on PSO-FCM optimized T-S model

  • Author

    Shuzhi, Gao ; Xing, Dou ; Xianwen, Gao

  • Author_Institution
    Coll. of Inf. Eng., Shenyang Univ. of Chem. Technol., Shenyang, China
  • fYear
    2012
  • fDate
    23-25 May 2012
  • Firstpage
    2529
  • Lastpage
    2532
  • Abstract
    In view of the characteristics of T-S model, such as easily expressing complex dynamic systems and the characteristics of PSO algorithm which could find the optimal solution of complex problems easily. This paper will presents a new identification method based on the T-S model in which FCM parameters is optimized by PSO. The mathematical model of the temperature system of the PVC stripper tower top will be built by this method. First, an adaptive number of clusters of C-means clustering fuzzy (FCM) algorithm is used to find the appropriate number of clusters in FCM, and both the number of fuzzy rules and the premise parameters of the model can are determined. Using PSO algorithm to optimize the FCM algorithm, then getting the best membership matrix by the FCM algorithm based on PSO in the end. Then, a least square algorithm is applied to determine the parameters of consequent part of T-S model. The simulation result shows the effectiveness and feasibility of the modeling method.
  • Keywords
    fuzzy set theory; identification; large-scale systems; least squares approximations; matrix algebra; particle swarm optimisation; pattern clustering; polymers; temperature; C-means clustering fuzzy algorithm; FCM algorithm; FCM parameters; PSO-FCM optimized T-S model; PVC stripper tower top; adaptive clusters number; complex dynamic systems; complex problems; fuzzy rules; least square algorithm; mathematical model; membership matrix; optimal solution; temperature system identification method; Adaptation models; Clustering algorithms; Data models; Educational institutions; Mathematical model; Optimization; Poles and towers; Particle swarm optimization algorithm (PSO); T-S model; fuzzy c-means clustering (FCM); least squares algorithm; polyvinyl chloride (PVC);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (CCDC), 2012 24th Chinese
  • Conference_Location
    Taiyuan
  • Print_ISBN
    978-1-4577-2073-4
  • Type

    conf

  • DOI
    10.1109/CCDC.2012.6243050
  • Filename
    6243050