Intelligent forecasting system based on grey model and neural network

Shih Hung Yang, Yon Ping Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper presents the design issues of two intelligent forecasting systems, feedforward-neural-networkaided grey model (FNAGM) and Elman-network-aided grey model (ENAGM). Both he FNAGM and ENAGM combine a first-order single variable grey model (GM(1,1)) and a neural network (NN). The GM(1,1) is adopted to predict signal, and the feedforward NN and the Elman network in the FNAGM and ENAGM respectively are used to learn the prediction error of the GM(1,1). Simulation results demonstrate that the intelligent forecasting systems with on-line learning can improve the prediction of the GM(1,1) and can be implemented in real-time prediction.

Original languageEnglish
Title of host publication2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
Pages699-704
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 4
Event2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 - Singapore, Singapore
Duration: 2009 Jul 142009 Jul 17

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Other

Other2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
CountrySingapore
CitySingapore
Period09-07-1409-07-17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Science Applications
  • Electrical and Electronic Engineering

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