Modelling and Identification of a Servo-mechanism using a Neural Network

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Modelling and Identification of a Servo-mechanism using a Neural Network

Popis výsledku v původním jazyce

The subject of this contribution is the use of neural networks for modeling and identification of processes. In the paper, a feedforward neural network is used. One of the advantages is that the resultant model depends on the setting of neural network weights. In the contribution, the Backpropagation algorithm was employed for this setting. Network topology plays also an important role and depends on the solved problem. Purpose of this paper is to propose a suitable topology of a neural network for a particular process and to compare the neural network model with real laboratory equipment as well. The Amira DR300 system depicted in the figure below was chosen as the laboratory model. It is a servo-mechanism which consists of a permanently excited DC-motor and a tacho-generator with the incremental encoder as a sensor of output signal. Input-output data were measured on this model and were further used for the neural network learning. Consequently the neural network describes the system

Název v anglickém jazyce

Modelling and Identification of a Servo-mechanism using a Neural Network

Popis výsledku anglicky

The subject of this contribution is the use of neural networks for modeling and identification of processes. In the paper, a feedforward neural network is used. One of the advantages is that the resultant model depends on the setting of neural network weights. In the contribution, the Backpropagation algorithm was employed for this setting. Network topology plays also an important role and depends on the solved problem. Purpose of this paper is to propose a suitable topology of a neural network for a particular process and to compare the neural network model with real laboratory equipment as well. The Amira DR300 system depicted in the figure below was chosen as the laboratory model. It is a servo-mechanism which consists of a permanently excited DC-motor and a tacho-generator with the incremental encoder as a sensor of output signal. Input-output data were measured on this model and were further used for the neural network learning. Consequently the neural network describes the system

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of XXXVth. Seminary ASR10 Instruments and Control

ISBN

978-80-248-2191-7

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

VŠB-Technická univerzita Ostrava

Místo vydání

Ostrava

Místo konání akce

Ostrava

Datum konání akce

1. 1. 2010

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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