Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications.

Journal: PloS one
Published Date: Dec 9, 2019

Abstract

AIM: Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. The network considers both global and local properties, deals with imprecision present in sensory data, leading to desired precisions. In this paper, we proposed a new FWNN model nominated "Fuzzy Jump Wavelet Neural Network" (FJWNN) for identifying dynamic nonlinear-linear systems, especially in practical applications.

Authors

  • Mohsen Kharazihai Isfahani
    Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
  • Maryam Zekri
    Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
  • Hamid Reza Marateb
    Biomedical Engineering DepartmentEngineering FacultyUniversity of Isfahan Isfahan 8415683111 Iran.
  • Miguel Angel Mañanas
    Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII) Universitat Politècnica de Catalunya-Barcelona Tech (UPC), Barcelona, Spain.

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