Construction and Optimization of Stability Conditions of Learning Processes in Mathematical Models of Neurodynamics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU150350" target="_blank" >RIV/00216305:26220/22:PU150350 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Construction and Optimization of Stability Conditions of Learning Processes in Mathematical Models of Neurodynamics
Original language description
This article is devoted to dynamic processes in the field of artificial intelligence, namely in the tasks of neurodynamics: the field of knowledge in which neural networks are considered as nonlinear dynamical systems and focuses on the problem of stability. The systems under consideration share four common characteristics: a large number of nodes (neurons), nonlinearity, dissipativity, noise. The purpose of this work is to build to construct of asymptotic stability conditions for dynamic model of neuronet network, which is described in terms of ODE nonlinear systems. Main method of investigation is Lyapunov direct method. Authors show that solution of pointed problem can be reduced to the task of convex optimization. By realization on Python tools the algorithm of Nelder-Mead method, a number of numerical experiments were conducted to select the optimal parameters of the Lyapunov function.
Czech name
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Czech description
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Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
9th International Scientific Conference "Information Technology and Implementation"
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
10
Pages from-to
1-10
Publisher name
CEUR-WS
Place of publication
neuveden
Event location
Kyiv
Event date
Nov 30, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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