Elliott waves classification by means of neural and pseudo neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F18%3A63520652" target="_blank" >RIV/70883521:28140/18:63520652 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/70883521:28140/16:43875582 RIV/61988987:17310/18:A1901TN2

Výsledek na webu

<a href="http://link.springer.com/article/10.1007/s00500-016-2097-y" target="_blank" >http://link.springer.com/article/10.1007/s00500-016-2097-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00500-016-2097-y" target="_blank" >10.1007/s00500-016-2097-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Elliott waves classification by means of neural and pseudo neural networks

Popis výsledku v původním jazyce

This article presents a comparative study of the classification of Elliott waves in data. Regarding the methods of classification, the paper deals with three approaches. The first one is a multilayer artificial neural network (ANN) with sigmoid activation function and backpropagation and Levenberg–Marquardt training algorithm. Second approach uses training algorithms of ANN but forms of activation functions of hidden nodes and nodes in output layers have been proposed by analytical programming with the differential evolution. The last approach offers results performed by synthesized pseudo neural networks where the symbolic regression is used for synthesis of a whole structure of the classifier, i.e., the relation between inputs and output(s) similar to ANN. In this case, meta-evolution version of analytic programming with differential evolution is used. In conclusion, all results of this experimental study were evaluated and compared mutually.

Název v anglickém jazyce

Elliott waves classification by means of neural and pseudo neural networks

Popis výsledku anglicky

This article presents a comparative study of the classification of Elliott waves in data. Regarding the methods of classification, the paper deals with three approaches. The first one is a multilayer artificial neural network (ANN) with sigmoid activation function and backpropagation and Levenberg–Marquardt training algorithm. Second approach uses training algorithms of ANN but forms of activation functions of hidden nodes and nodes in output layers have been proposed by analytical programming with the differential evolution. The last approach offers results performed by synthesized pseudo neural networks where the symbolic regression is used for synthesis of a whole structure of the classifier, i.e., the relation between inputs and output(s) similar to ANN. In this case, meta-evolution version of analytic programming with differential evolution is used. In conclusion, all results of this experimental study were evaluated and compared mutually.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA15-06700S" target="_blank" >GA15-06700S: Nekonvenční řízení komplexních systémů</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Soft computing

ISSN

1432-7643

e-ISSN

—

Svazek periodika

22

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

1803-1813

Kód UT WoS článku

000426761200007

EID výsledku v databázi Scopus

2-s2.0-84960157449