Hierarchical multi-dimensional differential evolution 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%2F12%3A86084510" target="_blank" >RIV/61989100:27240/12:86084510 - isvavai.cz</a>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network
Original language description
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
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2012
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
97
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
10
Pages from-to
131-140
UT code for WoS article
000309318200015
EID of the result in the Scopus database
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