Approximation and Prediction of Wages Based on Granular Neural Network
Result description
The article offers detailed computational algorithm used in this type of neural networks, extends their applications to fit and predict the data of economic time series, conducts experiments and indicates the gain of granular neural networks, specifically conducting experimentation using the classical (statistical) or econometric methods and conventional/soft RBF neural networks. Results are analysed and opportunities for future research are suggested.
Keywords
Probabilistic time-seriesClassic and soft RBF networkCloud modelsGranular computing
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Approximation and Prediction of Wages Based on Granular Neural Network
Original language description
The article offers detailed computational algorithm used in this type of neural networks, extends their applications to fit and predict the data of economic time series, conducts experiments and indicates the gain of granular neural networks, specifically conducting experimentation using the classical (statistical) or econometric methods and conventional/soft RBF neural networks. Results are analysed and opportunities for future research are suggested.
Czech name
Aproximace a predikce mezd založené na granulární euronové sítí
Czech description
Článek poskytuje detailní výpočtový algoritmus navrhnutý pro aproximaci a predikci časových řad prostřednictvím granulační RBF sítě. Je poskytnutá popsaná aplikace aproximace a predikce sítě na ekonomické časové řade. Současně je identifikován přínos vyvinuté metody oproti klasický statistickým, ekonometrickým modelům a modelům založeným a klasických (perceptronových) sítích. V závěru je načrtnut možný směr dalšího výzkumu v této oblasti.
Classification
Type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
AH - Economics
OECD FORD branch
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Result continuities
Project
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Rough Sets and Knowledge Technology. Third International Conference, RSKT 2008, Chengdu, China, May 2008, Proceedings
ISSN
0302-9743
e-ISSN
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Volume of the periodical
LNAI 5009
Issue of the periodical within the volume
2008
Country of publishing house
CN - CHINA
Number of pages
8
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
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Basic information
Result type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP
AH - Economics
Year of implementation
2009