Publication result detail

Closed Loop On-Line Identification Based on Neural Networks in Adaptive Optimal Controller

ŠVANCARA, K., PIVOŇKA, P.

Original Title

Closed Loop On-Line Identification Based on Neural Networks in Adaptive Optimal Controller

English Title

Closed Loop On-Line Identification Based on Neural Networks in Adaptive Optimal Controller

Type

Paper in proceedings (conference paper)

Original Abstract

Last ten years, algorithms based on Neural Networks were used successfully for the pattern recognition, process control and system identification. Artificial Neural Networks applied this way have been strongly developing together with the classical control. It is mainly because of their self-learning property and wide-range of easy algorithm designs. Using Neural Networks for identification is well-known strategy where the process is observed usually through its input and output only. The real process is often influenced by disturbances. In this case, the more identified transfer function is inaccurate the more as disturbance influences IO of the measured process. This paper shows a comparison between on-line identification (in the real time) based on Neural Networks and a classical identification implemented in adaptive optimal controller. The setting of the sampling period for the both identification methods is investigated.

English abstract

Last ten years, algorithms based on Neural Networks were used successfully for the pattern recognition, process control and system identification. Artificial Neural Networks applied this way have been strongly developing together with the classical control. It is mainly because of their self-learning property and wide-range of easy algorithm designs. Using Neural Networks for identification is well-known strategy where the process is observed usually through its input and output only. The real process is often influenced by disturbances. In this case, the more identified transfer function is inaccurate the more as disturbance influences IO of the measured process. This paper shows a comparison between on-line identification (in the real time) based on Neural Networks and a classical identification implemented in adaptive optimal controller. The setting of the sampling period for the both identification methods is investigated.

Keywords

On-Line neural identification, optimal adaptive controller,ARMA model, LD-FIL

Key words in English

On-Line neural identification, optimal adaptive controller,ARMA model, LD-FIL

Authors

ŠVANCARA, K., PIVOŇKA, P.

RIV year

2011

Released

04.09.2002

Publisher

Rektor der Hochschule Zittau/Görlitz

Location

Zittau, Německo

ISBN

3-9808089-2-0

Book

Proceedings East West Fuzzy Colloquium 2002

Pages from

218

Pages count

6

BibTex

@inproceedings{BUT4918,
  author="Kamil {Švancara} and Petr {Pivoňka}",
  title="Closed Loop On-Line Identification Based on Neural Networks in Adaptive Optimal Controller",
  booktitle="Proceedings East West Fuzzy Colloquium 2002",
  year="2002",
  pages="6",
  publisher="Rektor der Hochschule Zittau/Görlitz",
  address="Zittau, Německo",
  isbn="3-9808089-2-0"
}