• DocumentCode
    3220375
  • Title

    Prediction model of supply chain demand based on fuzzy neural network with chaotic time series

  • Author

    Wang Yan-Chen ; Zhang De-Gang ; Wang Xu

  • Author_Institution
    Coll. of Econ. & Manage. of Northeast, Forestry Univ., Harbin, China
  • fYear
    2013
  • fDate
    28-30 July 2013
  • Firstpage
    450
  • Lastpage
    455
  • Abstract
    In order to quickly determine and control the chaotic oscillation in supply chain system, to enhance the prediction accuracy of supply chain demand, and ensure the stability of supply chain systems, using fuzzy neural networks based on chaotic time series, sub-phase space is rebuilt by the demand time-series of supply chain system. Calculating the phase-space saturated embedding dimension and the largest Lyapunov index. Prediction model of supply chain demand has been built by fuzzy neural network based on a chaotic time series. The chaotic phenomena can be judged in supply chain system. Supply chain demand prediction controller has been designed based on fuzzy neural network. The simulating results show that fuzzy neural network with chaotic time series is feasible and effective on prediction of supply chain demand.
  • Keywords
    demand forecasting; fuzzy neural nets; supply chain management; time series; Lyapunov index; chaotic oscillation; chaotic time series; demand prediction controller; demand time-series; fuzzy neural network; phase-space saturated embedding dimension; prediction model; subphase space; supply chain demand; Chaos; Fuzzy neural networks; Neural networks; Neurons; Predictive models; Supply chains; Time series analysis; control; determine; fuzzy; neural networks; sopply chain;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Service Operations and Logistics, and Informatics (SOLI), 2013 IEEE International Conference on
  • Conference_Location
    Dongguan
  • Print_ISBN
    978-1-4799-0529-4
  • Type

    conf

  • DOI
    10.1109/SOLI.2013.6611457
  • Filename
    6611457