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

Komínková Oplatková, Zuzana; Šenkeřík, Roman

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.