Hierarchical particle swarm optimization for the design of beta basis function neural network
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Hierarchical particle swarm optimization for the design of beta basis function neural network
Original language 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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
—
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing. Volume 182
ISBN
978-3-642-32062-0
ISSN
2194-5357
e-ISSN
—
Number of pages
13
Pages from-to
193-205
Publisher name
Springer
Place of publication
Heidelberg
Event location
Chennai
Event date
Aug 4, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000315536100022