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Cognitive Neurodynamics

Erschienen in:

18.06.2022 | Research Article

Stability and synchronization for complex-valued neural networks with stochastic parameters and mixed time delays

verfasst von: Yufei Liu, Bo Shen, Jie Sun

Erschienen in: Cognitive Neurodynamics | Ausgabe 5/2023

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Abstract

In this paper, a class of complex-valued neural networks (CVNNs) with stochastic parameters and mixed time delays are proposed. The random fluctuation of system parameters is considered in order to describe the implementation of CVNNs more practically. Mixed time delays including distributed delays and time-varying delays are also taken into account in order to reflect the influence of network loads and communication constraints. Firstly, the stability problem is investigated for the CVNNs. In virtue of Lyapunov stability theory, a sufficient condition is deduced to ensure that CVNNs are asymptotically stable in the mean square. Then, for an array of coupled identical CVNNs with stochastic parameters and mixed time delays, synchronization issue is investigated. A set of matrix inequalities are obtained by using Lyapunov stability theory and Kronecker product and if these matrix inequalities are feasible, the addressed CVNNs are synchronized. Finally, the effectiveness of the obtained theoretical results is demonstrated by two numerical examples.

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Titel: Stability and synchronization for complex-valued neural networks with stochastic parameters and mixed time delays
verfasst von: Yufei Liu
Bo Shen
Jie Sun
Publikationsdatum: 18.06.2022
Verlag: Springer Netherlands
Erschienen in: Cognitive Neurodynamics / Ausgabe 5/2023
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI: https://doi.org/10.1007/s11571-022-09823-0

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