Pseudo Neural Networks for Iris Data Classification

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Pseudo Neural Networks for Iris Data Classification

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Pseudo Neural Networks for Iris Data Classification

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/ED2.1.00%2F03.0089" target="_blank" >ED2.1.00/03.0089: Centrum bezpečnostních, informačních a pokročilých technologií (CEBIA-Tech)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

28th European Conference on Modelling and Simulation

ISBN

978-0-9564944-8-1

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

387-392

Název nakladatele

ECMS

Místo vydání

Nottingham

Místo konání akce

Brescia

Datum konání akce

27. 5. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—