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

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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. 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. (en)
Title	Active fault detection for neural network based control of non-linear stochastic systems Active fault detection for neural network based control of non-linear stochastic systems (en)
skos:prefLabel	Active fault detection for neural network based control of non-linear stochastic systems Active fault detection for neural network based control of non-linear stochastic systems (en)
skos:notation	RIV/49777513:23520/09:43898283!RIV12-GA0-23520___
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(1M0572), P(GA102/08/0442), S
http://linked.open...iv/cisloPeriodika	1
http://linked.open...vai/riv/dodaniDat	2012
http://linked.open...aciTvurceVysledku	Punčochář, Ivo Šimandl, Miroslav Král, Ladislav
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Západočeská univerzita v Plzni / Fakulta aplikovaných věd
http://linked.open...dnocenehoVysledku	301845
http://linked.open...ai/riv/idVysledku	RIV/49777513:23520/09:43898283
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Stochastic systems, optimal control, change detection, optimal experiment design (en)
http://linked.open.../riv/klicoveSlovo	optimal experiment design optimal control Stochastic systems change detection
http://linked.open...odStatuVydavatele	ES - Španělské království
http://linked.open...ontrolniKodProRIV	[81F7D05941C0]
http://linked.open...i/riv/nazevZdroje	IFAC-PapersOnline
http://linked.open...in/vavai/riv/obor	BC
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Data, algorithms, decision making Feasible approximations of dual control strategies
http://linked.open...UplatneniVysledku	2009
http://linked.open...v/svazekPeriodika	Neuveden
http://linked.open...iv/tvurceVysledku	Punčochář, Ivo Šimandl, Miroslav Král, Ladislav
issn	1474-6670
number of pages	6 (xsd:int)
http://bibframe.org/vocab/doi	10.3182/20090630-4-ES-2003.00021
http://localhost/t...ganizacniJednotka	23520

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