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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2021

08.04.2021

Exponential Synchronization of Stochastic Neural Networks with Time-Varying Delays and Lévy Noises via Event-Triggered Control

verfasst von: Danni Lu, Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Jun Zhou, Shigen Shen

Erschienen in: Neural Processing Letters | Ausgabe 3/2021

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Abstract

This study is related to the exponential synchronization problem of stochastic neural networks. A dynamic model of master-slave neural networks is established, which contains time-varying delays and Lévy noises. The main purpose is to enable the slave system to follow the master system under the condition of limited communication capacity. Both the master system and the slave system are affected by random noises. Some sufficient conditions are given by means of linear matrix inequality methods which are established by applying Lyapunov functional together with the generalized Dynkin’s formula. Furthermore, a discrete event-triggered control is adopted in master-slave systems, which not only reduces the transmission resources but also avoids the Zeno phenomenon. At last, a numerical example is provided to verify the usefulness of judgment conditions in this study.
Vorheriger Artikel Multi-residual Connection Network for Edge Detection
Nächster Artikel Multi-Objective Memetic Algorithms with Tree-Based Genetic Programming and Local Search for Symbolic Regression

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Metadaten
Titel
Exponential Synchronization of Stochastic Neural Networks with Time-Varying Delays and Lévy Noises via Event-Triggered Control
verfasst von
Danni Lu
Dongbing Tong
Qiaoyu Chen
Wuneng Zhou
Jun Zhou
Shigen Shen
Publikationsdatum
08.04.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10509-7

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