Deep searching for parameter estimation of the linear time invariant (LTI) system by using Quasi-ARX neural network

Mohammad Abu Jami'In, Imam Sutrisno, Jinglu Hu

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

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2013 International Joint Conference on Neural Networks, IJCNN 2013
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
Duration: 2013 Aug 42013 Aug 9

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
CountryUnited States
CityDallas, TX
Period13/8/413/8/9

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Jami'In, M. A., Sutrisno, I., & Hu, J. (2013). Deep searching for parameter estimation of the linear time invariant (LTI) system by using Quasi-ARX neural network. In 2013 International Joint Conference on Neural Networks, IJCNN 2013 [6707091] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2013.6707091