Spatio-Temporal Data Classification using CVNNs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F13%3A00203405" target="_blank" >RIV/68407700:21240/13:00203405 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S1569190X12001347" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1569190X12001347</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.simpat.2012.10.001" target="_blank" >10.1016/j.simpat.2012.10.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spatio-Temporal Data Classification using CVNNs

Popis výsledku v původním jazyce

This paper presents two new approaches of spatio-temporal data classification using complex-valued neural networks. First approach uses extended complex-valued backpropagation algorithm to train MLP network, whose output?s amplitudes are encoded in one-of-N coding. It makes a classification decision based on accumulated distance between network output and trained pattern. The second approach is inspired in RBF networks with two layer architecture. Neurons from the first layer have fixed position in space and time encoded into theirs weights. This layer is trained by presented extension of neural gas algorithm into complex numbers. The second layer affects which neurons from the first layer belong to specific class. Paper contains details on experimenting with proposed approaches on artificial data of hand-written character recognition and comparison of both methods.

Název v anglickém jazyce

Spatio-Temporal Data Classification using CVNNs

Popis výsledku anglicky

This paper presents two new approaches of spatio-temporal data classification using complex-valued neural networks. First approach uses extended complex-valued backpropagation algorithm to train MLP network, whose output?s amplitudes are encoded in one-of-N coding. It makes a classification decision based on accumulated distance between network output and trained pattern. The second approach is inspired in RBF networks with two layer architecture. Neurons from the first layer have fixed position in space and time encoded into theirs weights. This layer is trained by presented extension of neural gas algorithm into complex numbers. The second layer affects which neurons from the first layer belong to specific class. Paper contains details on experimenting with proposed approaches on artificial data of hand-written character recognition and comparison of both methods.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Simulation Modelling Practice and Theory

ISSN

1569-190X

e-ISSN

—

Svazek periodika

33

Číslo periodika v rámci svazku

33

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

81-88

Kód UT WoS článku

000317253700007

EID výsledku v databázi Scopus

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