Forecast Models of Partial Differential Equations using Polynomial Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F13%3A86087575" target="_blank" >RIV/61989100:27740/13:86087575 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-01796-9_1" target="_blank" >http://dx.doi.org/10.1007/978-3-319-01796-9_1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-01796-9_1" target="_blank" >10.1007/978-3-319-01796-9_1</a>
Alternative languages
Result language
angličtina
Original language name
Forecast Models of Partial Differential Equations using Polynomial Networks
Original language description
Unknown data relations can describe lots of complex systems through partial differential equation solutions of a multi-parametric function approximation. Common neural network techniques of pattern classification or function approximation problems in general are based on whole-pattern similarity relationships of trained and tested data samples. They apply input variables of only absolute interval values, which may cause problems by far various training and testing data ranges. Differential polynomial neural network is a new type of neural network developed by the author, which constructs and substitutes an unknown general sum partial differential equation, defining a system model of dependent variables. It generates a total sum of fractional polynomialterms defining partial relative derivative dependent changes of some combinations of input variables. This type of regression is based only on trained generalized data relations. The character of relative data allows processing a wider r
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Opportunities for young researchers</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
Article name in the collection
Advances in Intelligent Systems and Computing. Volume 238
ISBN
978-3-319-01795-2
ISSN
2194-5357
e-ISSN
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Number of pages
11
Pages from-to
1-11
Publisher name
Springer
Place of publication
Berlin
Event location
Praha
Event date
Aug 25, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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