Pseudo Neural Networks via Analytic Programming with Direct Coding of Constant Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F18%3A63520634" target="_blank" >RIV/70883521:28140/18:63520634 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.scs-europe.net/dlib/2018/ecms2018acceptedpapers/0143_is_ecms2018_0861.pdf" target="_blank" >http://www.scs-europe.net/dlib/2018/ecms2018acceptedpapers/0143_is_ecms2018_0861.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7148/2018-0143" target="_blank" >10.7148/2018-0143</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pseudo Neural Networks via Analytic Programming with Direct Coding of Constant Estimation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Pseudo Neural Networks via Analytic Programming with Direct Coding of Constant Estimation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings - 32nd European Conference on Modelling and Simulation, ECMS 2018

ISBN

978-0-9932440-6-3

ISSN

2522-2414

e-ISSN

neuvedeno

Počet stran výsledku

7

Strana od-do

143-149

Název nakladatele

European Council for Modelling and Simulation

Místo vydání

Madrid

Místo konání akce

Wilhelmshaven

Datum konání akce

22. 5. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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