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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F02%3APU29163" target="_blank" >RIV/00216305:26220/02:PU29163 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 iis 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 onNeural Networks and a classical identification implemented in adaptive optimal controller. The setting of the sampling period for the both identification methods is investigated.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 iis 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 onNeural Networks and a classical identification implemented in adaptive optimal controller. The setting of the sampling period for the both identification methods is investigated.

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GA102%2F01%2F1485" target="_blank" >GA102/01/1485: Prostředí pro vývoj, modelování a aplikaci heterogenních systémů</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2002

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings East West Fuzzy Colloquium 2002

ISBN

3-9808089-2-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

218-223

Název nakladatele

Rektor der Hochschule Zittau/Görlitz

Místo vydání

Zittau, Německo

Místo konání akce

Zittau

Datum konání akce

4. 9. 2002

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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