TY - GEN
T1 - Integration of grey model and neural network for robotic application
AU - Yang, Shih-Hung
AU - Li, Jung Che
AU - Chen, Yon Ping
PY - 2011/12/1
Y1 - 2011/12/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84863050198
UR - https://www.scopus.com/pages/publications/84863050198#tab=citedBy
U2 - 10.1109/IECON.2011.6119682
DO - 10.1109/IECON.2011.6119682
M3 - Conference contribution
AN - SCOPUS:84863050198
SN - 9781612849720
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 2382
EP - 2387
BT - Proceedings
T2 - 37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011
Y2 - 7 November 2011 through 10 November 2011
ER -