Delay dependent complex-valued bidirectional associative memory neural networks with stochastic and impulsive effects: An exponential stability approach

Chinnamuniyandi Maharajan; Chandran Sowmiya; Changjin Xu

Kybernetika (2024)

  • Issue: 3, page 317-356
  • ISSN: 0023-5954

Abstract

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This paper investigates the stability in an exponential sense of complex-valued Bidirectional Associative Memory (BAM) neural networks with time delays under the stochastic and impulsive effects. By utilizing the contracting mapping theorem, the existence and uniqueness of the equilibrium point for the proposed complex-valued neural networks are verified. Moreover, based on the Lyapunov - Krasovskii functional construction, matrix inequality techniques and stability theory, some novel time-delayed sufficient conditions are attained in linear matrix inequalities (LMIs) form, which ensure the exponential stability of the trivial solution for the addressed neural networks. Finally, to illustrate the superiority and effects of our theoretical results, two numerical examples with their simulations are provided via MATLAB LMI control toolbox.

How to cite

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Maharajan, Chinnamuniyandi, Sowmiya, Chandran, and Xu, Changjin. "Delay dependent complex-valued bidirectional associative memory neural networks with stochastic and impulsive effects: An exponential stability approach." Kybernetika (2024): 317-356. <http://eudml.org/doc/299282>.

@article{Maharajan2024,
abstract = {This paper investigates the stability in an exponential sense of complex-valued Bidirectional Associative Memory (BAM) neural networks with time delays under the stochastic and impulsive effects. By utilizing the contracting mapping theorem, the existence and uniqueness of the equilibrium point for the proposed complex-valued neural networks are verified. Moreover, based on the Lyapunov - Krasovskii functional construction, matrix inequality techniques and stability theory, some novel time-delayed sufficient conditions are attained in linear matrix inequalities (LMIs) form, which ensure the exponential stability of the trivial solution for the addressed neural networks. Finally, to illustrate the superiority and effects of our theoretical results, two numerical examples with their simulations are provided via MATLAB LMI control toolbox.},
author = {Maharajan, Chinnamuniyandi, Sowmiya, Chandran, Xu, Changjin},
journal = {Kybernetika},
keywords = {Complex-valued neural networks; Linear matrix inequality; Lyapunov–Krasovskii functional; BAM neural networks; Exponential stability; Impulsive effects; Stochastic noise; discrete delays; distributed delays; leakage delays; mixed time delays},
language = {eng},
number = {3},
pages = {317-356},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Delay dependent complex-valued bidirectional associative memory neural networks with stochastic and impulsive effects: An exponential stability approach},
url = {http://eudml.org/doc/299282},
year = {2024},
}

TY - JOUR
AU - Maharajan, Chinnamuniyandi
AU - Sowmiya, Chandran
AU - Xu, Changjin
TI - Delay dependent complex-valued bidirectional associative memory neural networks with stochastic and impulsive effects: An exponential stability approach
JO - Kybernetika
PY - 2024
PB - Institute of Information Theory and Automation AS CR
IS - 3
SP - 317
EP - 356
AB - This paper investigates the stability in an exponential sense of complex-valued Bidirectional Associative Memory (BAM) neural networks with time delays under the stochastic and impulsive effects. By utilizing the contracting mapping theorem, the existence and uniqueness of the equilibrium point for the proposed complex-valued neural networks are verified. Moreover, based on the Lyapunov - Krasovskii functional construction, matrix inequality techniques and stability theory, some novel time-delayed sufficient conditions are attained in linear matrix inequalities (LMIs) form, which ensure the exponential stability of the trivial solution for the addressed neural networks. Finally, to illustrate the superiority and effects of our theoretical results, two numerical examples with their simulations are provided via MATLAB LMI control toolbox.
LA - eng
KW - Complex-valued neural networks; Linear matrix inequality; Lyapunov–Krasovskii functional; BAM neural networks; Exponential stability; Impulsive effects; Stochastic noise; discrete delays; distributed delays; leakage delays; mixed time delays
UR - http://eudml.org/doc/299282
ER -

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