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
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;
Conference_Titel :
Service Operations and Logistics, and Informatics (SOLI), 2013 IEEE International Conference on
Conference_Location :
Dongguan
Print_ISBN :
978-1-4799-0529-4
DOI :
10.1109/SOLI.2013.6611457