Detail publikace

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

DIBLÍK, J. SHATYRKO, A. KHUSAINOV, D. OLEKSII, B. BAŠTINEC, J.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Neuronet model; differential equation system; software; stability; Lyapunov function.

Autoři

DIBLÍK, J.; SHATYRKO, A.; KHUSAINOV, D.; OLEKSII, B.; BAŠTINEC, J.

Vydáno

2. 12. 2022

Nakladatel

CEUR-WS

ISSN

1613-0073

Periodikum

CEUR Workshop Proceedings

Stát

Spolková republika Německo

Strany od

1

Strany do

10

Strany počet

10

BibTex

@inproceedings{BUT187326,
  author="Andrej {Shatyrko} and Denys Ya. {Khusainov} and Bychkov {Oleksii} and Josef {Diblík} and Jaromír {Baštinec}",
  title="Construction and Optimization of Stability Conditions of Learning Processes in Mathematical Models of Neurodynamics",
  booktitle="9th International Scientific Conference {"}Information Technology and Implementation{"}",
  year="2022",
  journal="CEUR Workshop Proceedings",
  pages="1--10",
  publisher="CEUR-WS",
  issn="1613-0073"
}