Pseudo Neural Networks for Iris Data Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F14%3A43871643" target="_blank" >RIV/70883521:28140/14:43871643 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.7148/2014-0387" target="_blank" >http://dx.doi.org/10.7148/2014-0387</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7148/2014-0387" target="_blank" >10.7148/2014-0387</a>
Alternative languages
Result language
angličtina
Original language name
Pseudo Neural Networks for Iris Data Classification
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). 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
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)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
28th European Conference on Modelling and Simulation
ISBN
978-0-9564944-8-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
387-392
Publisher name
ECMS
Place of publication
Nottingham
Event location
Brescia
Event date
May 27, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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