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

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Hierarchical particle swarm optimization for the design of beta basis function neural network Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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. 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. (en)
Title	Hierarchical particle swarm optimization for the design of beta basis function neural network Hierarchical particle swarm optimization for the design of beta basis function neural network (en)
skos:prefLabel	Hierarchical particle swarm optimization for the design of beta basis function neural network Hierarchical particle swarm optimization for the design of beta basis function neural network (en)
skos:notation	RIV/61989100:27240/13:86092932!RIV15-MSM-27240___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Abraham Padath, Ajith
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / Fakulta elektrotechniky a informatiky
http://linked.open...dnocenehoVysledku	77314
http://linked.open...ai/riv/idVysledku	RIV/61989100:27240/13:86092932
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Time series prediction; Particle swarm; Nonlinear modeling; Hierarchical particle swarm optimization; Hierarchical level; Generalization performance; Free parameters; Different structure; Basis functions (en)
http://linked.open.../riv/klicoveSlovo	Particle swarm Time series prediction Basis functions Different structure Free parameters Generalization performance Hierarchical level Hierarchical particle swarm optimization Nonlinear modeling
http://linked.open...ontrolniKodProRIV	[0F843145FF90]
http://linked.open...v/mistoKonaniAkce	Chennai
http://linked.open...i/riv/mistoVydani	Heidelberg
http://linked.open...i/riv/nazevZdroje	Advances in Intelligent Systems and Computing. Volume 182
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Abraham Padath, Ajith Alimi, A. M. Dhahri, H.
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2012-08-04 (xsd:date)
issn	2194-5357
number of pages	13 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-642-32063-7_22
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-642-32062-0
http://localhost/t...ganizacniJednotka	27240

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software