Hybridized GA-optimization of Neural Dynamic Model for Nonlinear Process
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F12%3A00198066" target="_blank" >RIV/68407700:21220/12:00198066 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CarpathianCC.2012.6228644" target="_blank" >http://dx.doi.org/10.1109/CarpathianCC.2012.6228644</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CarpathianCC.2012.6228644" target="_blank" >10.1109/CarpathianCC.2012.6228644</a>
Alternative languages
Result language
angličtina
Original language name
Hybridized GA-optimization of Neural Dynamic Model for Nonlinear Process
Original language description
Neural networks as universal approximators possess capability to model complex nonlinear phenomena. However, when almost nothing is known about the modeled dynamic process it is difficult to determine important parameters like the number of neurons or the size of regressor vector (dynamic order). In order to avoid suboptimal settings for a dynamic model using trial-and-error method, genetic algorithm is used for optimizing the neural dynamic model. To improve the results even more, the genetic optimization is hybridized with a local optimizer in the form of Levenberg-Marquardt algorithm commonly used for neural network training. Here a neural model of biomass-fired boiler emissions is considered, which is eventually intended for predictive control. Series-parallel NARX model is used with two hidden layer neural network and tansigmoid transfer functions. The simpler neural model structure will be computationally less expensive what is important for online predictive control. The results
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JB - Sensors, detecting elements, measurement and regulation
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 13th International Carpathian Control Conference
ISBN
978-1-4577-1866-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
227-232
Publisher name
Technical University of Košice
Place of publication
Košice
Event location
Podbánské
Event date
May 28, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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