A Hybrid Learning Algorithm For Evolving Flexible Beta Basis Function Neural Tree Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86089345" target="_blank" >RIV/61989100:27240/13:86089345 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/13:86089345
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0925231213001975#" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231213001975#</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2013.01.024" target="_blank" >10.1016/j.neucom.2013.01.024</a>
Alternative languages
Result language
angličtina
Original language name
A Hybrid Learning Algorithm For Evolving Flexible Beta Basis Function Neural Tree Model
Original language description
In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operatorsets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimization algorithm (OPSO). The performance of the proposed method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related methods.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurocomputing
ISSN
0925-2312
e-ISSN
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Volume of the periodical
117
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
11
Pages from-to
107-117
UT code for WoS article
000321408200013
EID of the result in the Scopus database
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