Hierarchical particle swarm optimization 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%2F13%3A86092932" target="_blank" >RIV/61989100:27240/13:86092932 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-32063-7_22" target="_blank" >http://dx.doi.org/10.1007/978-3-642-32063-7_22</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-32063-7_22" target="_blank" >10.1007/978-3-642-32063-7_22</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical particle swarm optimization for the design of beta basis function neural network

Popis výsledku v původním jazyce

A novel learning algorithm is proposed for non linear modeling and identification by the use of the beta basis function neural network (BBFNN). The proposed method is a hierarchical particle swarm optimization (HPSO). The objective of this paper is to optimize the parameters of the beta basis function neural network (BBFNN) with high accuracy. The population of HPSO forms multiple beta neural networks with different structures at an upper hierarchical level and each particle of the previous population is optimized at a lower hierarchical level to improve the performance of each particle swarm. For the beta neural network consisting n particles 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 particle is to optimize the free parameters of the beta neural network. Experimental results on a number of benchmarks problems drawn from regression and time series prediction area demonstrate that the HPSO produces a better generalization performance. 2013 Springer-Verlag.

Název v anglickém jazyce

Hierarchical particle swarm optimization for the design of beta basis function neural network

Popis výsledku anglicky

A novel learning algorithm is proposed for non linear modeling and identification by the use of the beta basis function neural network (BBFNN). The proposed method is a hierarchical particle swarm optimization (HPSO). The objective of this paper is to optimize the parameters of the beta basis function neural network (BBFNN) with high accuracy. The population of HPSO forms multiple beta neural networks with different structures at an upper hierarchical level and each particle of the previous population is optimized at a lower hierarchical level to improve the performance of each particle swarm. For the beta neural network consisting n particles 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 particle is to optimize the free parameters of the beta neural network. Experimental results on a number of benchmarks problems drawn from regression and time series prediction area demonstrate that the HPSO produces a better generalization performance. 2013 Springer-Verlag.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 182

ISBN

978-3-642-32062-0

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

13

Strana od-do

193-205

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Chennai

Datum konání akce

4. 8. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000315536100022