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

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2013 International Joint Conference on Neural Networks, IJCNN 2013
DOI
出版ステータスPublished - 2013 12月 1
イベント2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
継続期間: 2013 8月 42013 8月 9

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Conference

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

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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