Intelligent forecasting system using grey model combined with neural network

Shih Hung Yang*, Yon-Ping Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

This paper proposes an intelligent forecasting system based on a feedforward-neural-network-aided grey model (FNAGM), which integrates a first-order single variable grey model (GM(1,1)) and a feedfor-ward neural network. There are three phases in the system process, including initialization phase, GM(1,1) prediction phase and FNAGM prediction phase. First, some parameters required in the FNAGM are chosen in the initialization phase. Then, a one-step-ahead predictive value is generated in the GM(1,1) prediction phase. Finally, a feedfor-ward neural network is used to learn the prediction error of the GM(1,1) and compensate it in the FNAGM prediction phase. Significantly, an on-line batch training is adopted to adjust the network according to the Levenberg-Marquardt algorithm in real-time. From the simulation results, the proposed intelligent forecasting system indeed improves the prediction error of the GM(1,1) and obtains more accurate prediction than other numerical methods.

Original languageEnglish
Pages (from-to)8-15
Number of pages8
JournalInternational Journal of Fuzzy Systems
Volume13
Issue number1
DOIs
StatePublished - 1 Mar 2011

Keywords

  • Batch training
  • Grey model
  • Neural network
  • On-line training
  • Prediction

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