A substitution of the general partial differential equation with extended polynomial networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099062" target="_blank" >RIV/61989100:27240/16:86099062 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7727833" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7727833</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2016.7727833" target="_blank" >10.1109/IJCNN.2016.7727833</a>

Result language
angličtina
Original language name
A substitution of the general partial differential equation with extended polynomial networks
Original language description
General partial differential equations, which can describe any complex functions, may be solved by an adapted method of the similarity analysis that models polynomial data relations of discrete observations. The proposed new differential polynomial networks define and substitute for a selective form of the general partial differential equation using fraction derivative units to model an unknown system or pattern. Convergent series of relative derivative substitution terms, produced in all network layers, describe the partial derivative changes of some combinations of input variables to generalize elementary polynomial data relations. The general differential equation is decomposed into polynomial network backward structure, which defines simple and composite sum derivative terms in respect of previous layers variables. The proposed method enables to form more complex and varied derivative selective series models than standard soft-computing techniques. The sigmoidal function, commonly employed as an activation function in artificial neurons, may improve the abilities of the polynomial networks and substituting derivative terms to approximate complicated periodic multi-variable or time-series functions in a system model. (C) 2016 IEEE.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the International Joint Conference on Neural Networks
ISBN
978-1-5090-0619-9
ISSN
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e-ISSN
—
Number of pages
8
Pages from-to
4819-4826
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
New York
Event location
Vancouver
Event date
Jul 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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