Iris Data Classification by Means of Pseudo Neural Networks Based on Evolutionary Symbolic Regression

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F13%3A43870586" target="_blank" >RIV/70883521:28140/13:43870586 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Iris Data Classification by Means of Pseudo Neural Networks Based on Evolutionary Symbolic Regression
Original language description
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 numericalweights, 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) and tested on iris data in this case. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used.
Czech name
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Czech description
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Project
<a href="/en/project/ED2.1.00%2F03.0089" target="_blank" >ED2.1.00/03.0089: The Centre of Security, Information and Advanced Technologies (CEBIA-Tech)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
27th European Conference on Modelling and Simulation
ISBN
978-0-9564944-6-7
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
355-360
Publisher name
ECMS
Place of publication
Alesund
Event location
Alesund
Event date
May 27, 2013
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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