Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86084510" target="_blank" >RIV/61989100:27240/12:86084510 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.neucom.2012.04.008" target="_blank" >http://dx.doi.org/10.1016/j.neucom.2012.04.008</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2012.04.008" target="_blank" >10.1016/j.neucom.2012.04.008</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network

Popis výsledku v původním jazyce

This paper proposes a hierarchical multi-dimensional differential evolution (HMDDE) algorithm, which is an automatic computational frame work for the optimization of beta basis function neural network (BBFNN) wherein the neural network architecture, weights connection, learning algorithm and its parameters are adapted according to the problem. In the HMDDE-designed neural network, the number of individuals of the population multi-dimensions is the number of beta neural networks. The population of HMDDEforms multiple beta networks with different structures at the higher level and each individual of the previous population is optimized at a lower hierarchical level to improve the performance of each individual. For the beta neural network consisting ofm neurons, n individuals (different lengths) are formed in the upper level to optimize the structure of the beta neural network. In the lower level, the population within the same length is to optimize the free parameters of the beta neur

Název v anglickém jazyce

Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network

Popis výsledku anglicky

This paper proposes a hierarchical multi-dimensional differential evolution (HMDDE) algorithm, which is an automatic computational frame work for the optimization of beta basis function neural network (BBFNN) wherein the neural network architecture, weights connection, learning algorithm and its parameters are adapted according to the problem. In the HMDDE-designed neural network, the number of individuals of the population multi-dimensions is the number of beta neural networks. The population of HMDDEforms multiple beta networks with different structures at the higher level and each individual of the previous population is optimized at a lower hierarchical level to improve the performance of each individual. For the beta neural network consisting ofm neurons, n individuals (different lengths) are formed in the upper level to optimize the structure of the beta neural network. In the lower level, the population within the same length is to optimize the free parameters of the beta neur

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

97

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

131-140

Kód UT WoS článku

000309318200015

EID výsledku v databázi Scopus

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