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Pseudo neural networks via analytic programming with direct coding of constant estimation

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dc.title Pseudo neural networks via analytic programming with direct coding of constant estimation en
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Proceedings - European Council for Modelling and Simulation, ECMS
dc.identifier.issn 2522-2414 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-0-9932440-6-3
dc.date.issued 2018
dc.citation.spage 143
dc.citation.epage 149
dc.event.title 32nd Annual Conference of the European Conference on Modelling and Simulation, ECMS 2018
dc.event.location Wilhelmshaven
utb.event.state-en Germany
utb.event.state-cs Německo
dc.event.sdate 2018-05-22
dc.event.edate 2018-05-25
dc.type conferenceObject
dc.language.iso en
dc.publisher European Council for Modelling and Simulation
dc.identifier.doi 10.7148/2018-0143
dc.relation.uri http://www.scs-europe.net/dlib/2018/2018-0143.htm
dc.relation.uri http://www.scs-europe.net/dlib/2018/ecms2018acceptedpapers/0143_is_ecms2018_0861.pdf
dc.subject pseudo neural networks en
dc.subject analytic programming en
dc.subject differential evolution en
dc.description.abstract This research deals with a novel approach to classification - pseudo neural networks (PNN). This technique was inspired in classical artificial neural networks (ANN), where a relation between inputs and outputs is based on the mathematical transfer functions and optimised numerical weights. Compared to ANN, the whole structure in PNN, i.e. the relation between inputs and output(s), is fully synthesised by evolutionary symbolic regression tool - analytic programming. Compared to previous synthesised models, the PNN in this paper were synthesised via a new approach to constant estimation inside the analytic programming - direct coding. Iris data was used for the experiments and PNN were used for the synthesis of a complex classifier for more classes. For experimentation, Differential Evolution (de/rand/1/bin) for optimisation in analytic programming (AP) was used. © ECMS Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008574
utb.identifier.obdid 43879080
utb.identifier.scopus 2-s2.0-85062836704
utb.identifier.wok 000656119000021
utb.source d-scopus
dc.date.accessioned 2019-07-08T11:59:56Z
dc.date.available 2019-07-08T11:59:56Z
dc.description.sponsorship Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [CA15140, IC1406]; A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Zuzana Kominkova Oplatkova, Adam Viktorin, Roman Senkerik Tomas Bata University in Zlin, Faculty of Applied Informatics Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {oplatkova, aviktorin, senkerik}@.utb.cz
utb.fulltext.dates -
utb.wos.affiliation [Oplatkova, Zuzana Kominkova; Viktorin, Adam; Senkerik, Roman] Tomas Bata Univ Zlin, Fac Appl Info Lat, Nam TG Masaryka 5555, Zlin 76001, Czech Republic
utb.scopus.affiliation Tomas Bata University in Zlin, Faculty of Applied Informatics, Nam T.G. Masaryka 5555, Zlin, 760 01, Czech Republic
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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