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Pseudo neural networks for Iris data classification

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dc.title Pseudo neural networks for Iris data classification en
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Komínek, Aleš
dc.relation.ispartof Proceedings - 28th European Conference on Modelling and Simulation, ECMS 2014
dc.identifier.isbn 978-0-9564944-8-1
dc.date.issued 2014
dc.citation.spage 387
dc.citation.epage 392
dc.event.title 28th European Conference on Modelling and Simulation, ECMS 2014
dc.event.location Brescia
utb.event.state-en Italy
utb.event.state-cs Itálie
dc.event.sdate 2014-05-27
dc.event.edate 2014-05-30
dc.type conferenceObject
dc.language.iso en
dc.publisher European Council for Modelling and Simulation
dc.identifier.doi 10.7148/2014-0387
dc.relation.uri http://www.scs-europe.net/dlib/2014/2014-0387.htm
dc.relation.uri http://www.scs-europe.net/dlib/2014/ecms14papers/is_ECMS2014_0149.pdf
dc.subject Analytic programming en
dc.subject Differential Evolution en
dc.subject Iris data en
dc.subject Pseudo Neural Network en
dc.description.abstract This research deals with a novel approach to classification. Iris data was used for the experiments. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, was an inspiration for this work. Artificial neural networks need to optimize weights, but the structure and transfer functions are usually set up before the training. The proposed method utilizes the symbolic regression for synthesis of a whole structure, i.e. the relation between inputs and output(s). This paper differs from the previous approach where only one output pseudo node was used even for more classes. In this case, there were synthesized more node output equations as in classical artificial neural networks. The benchmark was iris data as in previous research. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used. Proceedings 28th European Conference on Modelling and Simulation © ECMS Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani (Editors). en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004616
utb.identifier.obdid 43871862
utb.identifier.scopus 2-s2.0-84905754109
utb.source d-scopus
dc.date.accessioned 2015-06-04T12:54:25Z
dc.date.available 2015-06-04T12:54:25Z
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Komínek, Aleš
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