Integration of grey model and neural network for robotic application

Shih Hung Yang*, Jung Che Li, Yon-Ping Chen

*Corresponding author for this work

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

2 Scopus citations

Abstract

This paper proposes an intelligent forecasting system based on a feedforward neural network aided grey model (FNAGM), integrating a first-order single variable grey model (GM(1,1)) and a feedforward neural network. The system includes three phases: initialization phase, GM(1,1) prediction phase, and FNAGM prediction phase. A number of parameters required for the FNAGM are selected in the initialization phase. A one-step ahead predictive value is generated in the GM(1,1) prediction phase, followed by the implementation of a feedforward neural network used to determine the prediction error of the GM(1,1) and compensate for it in the FNAGM prediction phase. We also adopted on-line batch training to adjust the network according to the Levenberg-Marquardt algorithm in real-time. According to the experimental results of a robot, the proposed intelligent forecasting system can provide high accuracy for both trajectory prediction and target tracking.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society
Pages2382-2387
Number of pages6
DOIs
StatePublished - 1 Dec 2011
Event37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011 - Melbourne, VIC, Australia
Duration: 7 Nov 201110 Nov 2011

Publication series

NameIECON Proceedings (Industrial Electronics Conference)

Conference

Conference37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011
CountryAustralia
CityMelbourne, VIC
Period7/11/1110/11/11

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  • Cite this

    Yang, S. H., Li, J. C., & Chen, Y-P. (2011). Integration of grey model and neural network for robotic application. In Proceedings: IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society (pp. 2382-2387). [6119682] (IECON Proceedings (Industrial Electronics Conference)). https://doi.org/10.1109/IECON.2011.6119682