TY - GEN
T1 - Shuttlecock flight trajectory modeling and analysis using linear and neural network ARX
AU - Lubis, Muhammad Firdaus Syawaludin
AU - Hidayat, Dean Zaka
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/24
Y1 - 2015/11/24
N2 - In this paper, a shuttlecock flight trajectory model using Linear ARX and Neural Network ARX (NARX) is proposed and compared. Several ARX and Neural Network ARX model configurations were created. Every Linear ARX and Neural Network ARX model configurations differ in term of number of autoregressive and exogenous components. Every model configurations used the horizontal trajectory as inputs and flight trajectory as output. The experiments showed that, ARX and Neural Network ARX could predict the flight trajectories 80 - 90% and simulate the flight trajectories 60 - 70%. In addition, it also shows that if the regressor was chosen properly, the Neural Network ARX would outperform the Linear ARX. Nonetheless, the wrong choice of autoregressive and exogenous components will lower the Neural Network ARX model configuration performance significantly. On the contrary, although still affected as well, Linear ARX models were not as vulnerable as Neural Network ARX models in term of choice of autoregressive and exogenous components they have.
AB - In this paper, a shuttlecock flight trajectory model using Linear ARX and Neural Network ARX (NARX) is proposed and compared. Several ARX and Neural Network ARX model configurations were created. Every Linear ARX and Neural Network ARX model configurations differ in term of number of autoregressive and exogenous components. Every model configurations used the horizontal trajectory as inputs and flight trajectory as output. The experiments showed that, ARX and Neural Network ARX could predict the flight trajectories 80 - 90% and simulate the flight trajectories 60 - 70%. In addition, it also shows that if the regressor was chosen properly, the Neural Network ARX would outperform the Linear ARX. Nonetheless, the wrong choice of autoregressive and exogenous components will lower the Neural Network ARX model configuration performance significantly. On the contrary, although still affected as well, Linear ARX models were not as vulnerable as Neural Network ARX models in term of choice of autoregressive and exogenous components they have.
KW - ARX
KW - Model Identification
KW - Neural Network
KW - Shuttlecock
KW - Trajectory Tracking
UR - http://www.scopus.com/inward/record.url?scp=84960943848&partnerID=8YFLogxK
U2 - 10.1109/ICCEREC.2015.7337057
DO - 10.1109/ICCEREC.2015.7337057
M3 - Conference contribution
AN - SCOPUS:84960943848
T3 - ICCEREC 2015 - International Conference on Control, Electronics, Renewable Energy and Communications
SP - 70
EP - 74
BT - ICCEREC 2015 - International Conference on Control, Electronics, Renewable Energy and Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Control, Electronics, Renewable Energy and Communications, ICCEREC 2015
Y2 - 27 August 2015 through 28 August 2015
ER -
