TY - GEN
T1 - Deep searching for parameter estimation of the linear time invariant (LTI) system by using Quasi-ARX neural network
AU - Jami'In, Mohammad Abu
AU - Sutrisno, Imam
AU - Hu, Jinglu
PY - 2013/12/1
Y1 - 2013/12/1
N2 - This work exploits the idea on how to search parameter estimation and increase its convergence speed for the Liner Time Invariant (LTI) system. The convergence speed of parameter estimation is the one problem and plays an important role in the adaptive controller to increase performance. The well-known algorithm is the recursive least square algorithm. However, the speed of convergence is still low and is influenced by the number of sampling, which is represented by the limited availability for the information vector. We offer a new method to increase the convergence speed by applying Quasi-ARX model. Quasi-ARX model performs two steps identification process by presenting parameter estimation as a function over time. The first, parameters estimation of macro-part sub-model are searched by the least square error, and the second is to sharpen the searching by performing backpropagation learning of multi layer parceptron network.
AB - This work exploits the idea on how to search parameter estimation and increase its convergence speed for the Liner Time Invariant (LTI) system. The convergence speed of parameter estimation is the one problem and plays an important role in the adaptive controller to increase performance. The well-known algorithm is the recursive least square algorithm. However, the speed of convergence is still low and is influenced by the number of sampling, which is represented by the limited availability for the information vector. We offer a new method to increase the convergence speed by applying Quasi-ARX model. Quasi-ARX model performs two steps identification process by presenting parameter estimation as a function over time. The first, parameters estimation of macro-part sub-model are searched by the least square error, and the second is to sharpen the searching by performing backpropagation learning of multi layer parceptron network.
UR - http://www.scopus.com/inward/record.url?scp=84893587289&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893587289&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2013.6707091
DO - 10.1109/IJCNN.2013.6707091
M3 - Conference contribution
AN - SCOPUS:84893587289
SN - 9781467361293
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013
T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013
Y2 - 4 August 2013 through 9 August 2013
ER -