Constructing General Partial Differential Equations using Polynomial and Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86095964" target="_blank" >RIV/61989100:27240/16:86095964 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27730/16:86095964
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0893608015001999" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0893608015001999</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neunet.2015.10.001" target="_blank" >10.1016/j.neunet.2015.10.001</a>
Alternative languages
Result language
angličtina
Original language name
Constructing General Partial Differential Equations using Polynomial and Neural Networks
Original language description
Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems.
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)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Networks
ISSN
0893-6080
e-ISSN
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Volume of the periodical
73
Issue of the periodical within the volume
jaro
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
"58-69"
UT code for WoS article
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EID of the result in the Scopus database
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