Weight adaptation stability of linear and higher-order neural units for prediction applications

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F19%3A00328607" target="_blank" >RIV/68407700:21220/19:00328607 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-319-98678-4_50" target="_blank" >https://doi.org/10.1007/978-3-319-98678-4_50</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-98678-4_50" target="_blank" >10.1007/978-3-319-98678-4_50</a>

Result language
angličtina
Original language name
Weight adaptation stability of linear and higher-order neural units for prediction applications
Original language description
This paper is focused on weight adaptation stability analysis of static and dynamic neural units for prediction applications. The aim of this paper is to provide verifiable conditions in which the weight system is stable during sample-by-sample adaptation. The paper presents a novel approach toward stability of linear and higher-order neural units. A study of utilization of linear and higher-order neural units with the foundations on stability of the gradient descent algorithm for static and dynamic models is addressed.
Czech name
—
Czech description
—

Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)