Finite-time Synchronization of Delayed Inertial Neural Networks via Intermittent Control

This paper introduces the inertial neural network model with time-delays at first. Through reduced-order approach, the second-order neural networks can be transformed into two first-order differential equations, which simplify the models. Then, by designing the periodically intermittent control strategy, some algebraic conditions for the finite-time synchronization of delayed inertial neural networks are derived by virtue of finite time stability theorems and inequality techniques. What's more, the constructed controller here is independent with delays. Compared with delay-dependent controller, the controller herein is more suitable for practical applications. Moreover, the settling time of delayed inertial neural networks is estimated. In addition, a numerical example is given to verify the effectiveness of the criteria.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1109/ntci64025.2024.10776514
Citations
1
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.35
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Finite-time Synchronization of Delayed Inertial Neural Networks via Intermittent Control

Yuxin Jiang, Song Chun Zhu
1 citations
Neural Networks Stability and Synchronization
0.35
article

Finite-time Synchronization of Delayed Inertial Neural Networks via Intermittent Control

Yuxin Jiang, Song Chun Zhu
article en
1 citations

Abstract

This paper introduces the inertial neural network model with time-delays at first. Through reduced-order approach, the second-order neural networks can be transformed into two first-order differential equations, which simplify the models. Then, by designing the periodically intermittent control strategy, some algebraic conditions for the finite-time synchronization of delayed inertial neural networks are derived by virtue of finite time stability theorems and inequality techniques. What's more, the constructed controller here is independent with delays. Compared with delay-dependent controller, the controller herein is more suitable for practical applications. Moreover, the settling time of delayed inertial neural networks is estimated. In addition, a numerical example is given to verify the effectiveness of the criteria.

China University of Mining and Technology (CN)
Openalex Percentile: Top 36%
Neural Networks Stability and Synchronization
0.35
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