Active fault detection for neural network based control of non-linear stochastic systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F09%3A43898283" target="_blank" >RIV/49777513:23520/09:43898283 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3182/20090630-4-ES-2003.00021" target="_blank" >http://dx.doi.org/10.3182/20090630-4-ES-2003.00021</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3182/20090630-4-ES-2003.00021" target="_blank" >10.3182/20090630-4-ES-2003.00021</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Active fault detection for neural network based control of non-linear stochastic systems

Popis výsledku v původním jazyce

This paper deals with design of active fault detection of non-linear stochastic systems. As general solution of the problem is extremely difficult, a special case of active detector design for a given set of controllers for jump Markov non-linear Gaussian models is considered. The optimal active detector for a given set of controllers is intractable and therefore, the rolling horizon technique will be used to reduce computational costs. The system is modelled using a multi-layer perceptron neural network where structure and unknown parameters are obtained by means of an off-line training process based on the extended Kalman filter estimation method and structure optimization using pruning of the insignificant connections. The proposed active detector is compared with a passive one based on open-loop feedback strategy and the performance is illustrated in an example by simulation and Monte Carlo analysis.

Název v anglickém jazyce

Active fault detection for neural network based control of non-linear stochastic systems

Popis výsledku anglicky

This paper deals with design of active fault detection of non-linear stochastic systems. As general solution of the problem is extremely difficult, a special case of active detector design for a given set of controllers for jump Markov non-linear Gaussian models is considered. The optimal active detector for a given set of controllers is intractable and therefore, the rolling horizon technique will be used to reduce computational costs. The system is modelled using a multi-layer perceptron neural network where structure and unknown parameters are obtained by means of an off-line training process based on the extended Kalman filter estimation method and structure optimization using pruning of the insignificant connections. The proposed active detector is compared with a passive one based on open-loop feedback strategy and the performance is illustrated in an example by simulation and Monte Carlo analysis.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

IFAC-PapersOnline

ISSN

1474-6670

e-ISSN

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Svazek periodika

Neuveden

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

ES - Španělské království

Počet stran výsledku

6

Strana od-do

125-130

Kód UT WoS článku

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EID výsledku v databázi Scopus

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