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Control law and pseudo neural networks synthesized by evolutionary symbolic regression technique

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dc.title Control law and pseudo neural networks synthesized by evolutionary symbolic regression technique en
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Seminal Contributions to Modelling and Simulation: 30 Years of the European Council of Modelling and Simulation
dc.identifier.issn 2195-2817 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2016
dc.citation.spage 91
dc.citation.epage 113
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer International Publishing AG
dc.identifier.doi 10.1007/978-3-319-33786-9_9
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-33786-9_9
dc.subject Analytic programming en
dc.subject Differential evolution en
dc.subject Control law en
dc.subject Pseudo neural network en
dc.description.abstract This research deals with synthesis of final complex expressions by means of an evolutionary symbolic regression technique-analytic programming (AP)for novel approach to classification and system control. In the first case, classification technique-pseudo neural network is synthesized, i. e. relation between inputs and outputs created. The inspiration came from classical artificial neural networks where such a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights. AP will synthesize a whole expression at once. The latter case, the AP will create chaotic controller that secures the stabilization of stable state and high periodic orbit-oscillations between several values of discrete chaotic system. Both cases will produce a mathematical relation with several inputs, the latter case uses several historical values from the time series. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007409
utb.identifier.obdid 43876368
utb.identifier.wok 000389485000010
utb.source d-wok
dc.date.accessioned 2017-09-08T12:14:53Z
dc.date.available 2017-09-08T12:14:53Z
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Zuzana Kominkova Oplatkova and Roman Senkerik Z.K. Oplatkova (✉) R. Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic e-mail: oplatkova@fai.utb.cz R. Senkerik e-mail: senkerik@fai.utb.cz
utb.fulltext.dates -
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utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, further it was supported by Grant Agency of the Czech Republic—GACR 588P103/15/06700S.
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