Intelligent forecasting system based on grey model and neural network

Shih Hung Yang; Yon Ping Chen

doi:10.1109/AIM.2009.5229929

Intelligent forecasting system based on grey model and neural network

Shih Hung Yang, Yon Ping Chen

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
Pages	699-704
Number of pages	6
DOIs	https://doi.org/10.1109/AIM.2009.5229929
Publication status	Published - 2009
Event	2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 - Singapore, Singapore Duration: 2009 Jul 14 → 2009 Jul 17

Publication series

Name	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Other

Other	2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
Country/Territory	Singapore
City	Singapore
Period	09-07-14 → 09-07-17

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1109/AIM.2009.5229929

Cite this

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title = "Intelligent forecasting system based on grey model and neural network",

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.",

author = "Yang, {Shih Hung} and Chen, {Yon Ping}",

year = "2009",

doi = "10.1109/AIM.2009.5229929",

language = "English",

isbn = "9781424428533",

series = "IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM",

pages = "699--704",

booktitle = "2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009",

note = "2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 ; Conference date: 14-07-2009 Through 17-07-2009",

}

Yang, SH & Chen, YP 2009, Intelligent forecasting system based on grey model and neural network. in 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009., 5229929, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, pp. 699-704, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009, Singapore, Singapore, 09-07-14. https://doi.org/10.1109/AIM.2009.5229929

Intelligent forecasting system based on grey model and neural network. / Yang, Shih Hung; Chen, Yon Ping.
2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009. 2009. p. 699-704 5229929 (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Chen, Yon Ping

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Intelligent forecasting system based on grey model and neural network

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