TY - GEN
T1 - Hierarchical algorithm for the identification of parameter estimation of linear system
AU - Jami'in, Mohammad Abu
AU - Anam, Khairul
AU - Rulaningtyas, Riries
AU - Echsony, Mohammaderik
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - A novel technique to identification of autoregressive moving average (ARMA)systems is proposed to increase the accuracy and speed of convergence for the system identification. The convergence speed of recursive least square algorithm (RLS) is solved under differential equations that needs all necessary information about the asymptotic behavior. Using RLS estimation, the convergence of parameters is able to the true values if the data of information vector growing to infinite. Therefore, the convergence of the parameters of the RLS algorithm takes time or needs a large number of sampling. In order to improve the accuracy and convergence speed of the estimated parameters, we propose a technique that modifies the QARXNN model by running two steps to identify the system hierarchically. The proposed method performs two steps: first, the system is identified by least square error (LSE) algorithm. Second, performs multi-input multi-output feedforward neural networks (MIMO-NN) to refine the estimated parameters by updating the parameters based on the residual error of LSE. The residual error by using LSE is set as target output to train NN. Finally, we illustrate and verify the proposed technique with an experimental studies. The proposed method can find the estimated parameters faster with = 0.935129 % in tenth sampling. The results is almost consistence which the accuracy of the identified parameters did not change significantly with the increasing number of sampling or the number of data points.
AB - A novel technique to identification of autoregressive moving average (ARMA)systems is proposed to increase the accuracy and speed of convergence for the system identification. The convergence speed of recursive least square algorithm (RLS) is solved under differential equations that needs all necessary information about the asymptotic behavior. Using RLS estimation, the convergence of parameters is able to the true values if the data of information vector growing to infinite. Therefore, the convergence of the parameters of the RLS algorithm takes time or needs a large number of sampling. In order to improve the accuracy and convergence speed of the estimated parameters, we propose a technique that modifies the QARXNN model by running two steps to identify the system hierarchically. The proposed method performs two steps: first, the system is identified by least square error (LSE) algorithm. Second, performs multi-input multi-output feedforward neural networks (MIMO-NN) to refine the estimated parameters by updating the parameters based on the residual error of LSE. The residual error by using LSE is set as target output to train NN. Finally, we illustrate and verify the proposed technique with an experimental studies. The proposed method can find the estimated parameters faster with = 0.935129 % in tenth sampling. The results is almost consistence which the accuracy of the identified parameters did not change significantly with the increasing number of sampling or the number of data points.
KW - System identification
KW - convergence speed
KW - hierarchical algorithm
KW - parameter estimation
KW - quasi-linear ARX neural network
UR - http://www.scopus.com/inward/record.url?scp=85065259048&partnerID=8YFLogxK
U2 - 10.1109/SIET.2018.8693176
DO - 10.1109/SIET.2018.8693176
M3 - Conference contribution
AN - SCOPUS:85065259048
T3 - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
SP - 71
EP - 76
BT - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018
Y2 - 10 November 2018 through 12 November 2018
ER -