Finite‐Time and Fixed‐Time Synchronization of Memristor‐based Neural Networks With Proportional Delays via Chattering‐free Quantized Control

ABSTRACT The study designed two chattering‐free quantized controllers aimed at achieving finite‐time and fixed‐time synchronization of memristive neural networks (MNNs) based on quantized control. These controllers are not only easy to implement but also effectively handle the mismatched uncertainties and proportional delays in Filippov solutions, ensuring chattering‐free operation. By introducing a new Lyapunov function and using the 1‐norm method, the study derived several sufficient conditions to guarantee drive‐response synchronization of MNNs within finite or fixed time. To verify the accuracy of these theoretical results, numerical simulations were conducted, demonstrating the effectiveness and reliability of the proposed methods.

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Publication Details

Journal
Asian Journal of Control
Published
2026-10-30
DOI
https://doi.org/10.1002/asjc.70284
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

Finite‐Time and Fixed‐Time Synchronization of Memristor‐based Neural Networks With Proportional Delays via Chattering‐free Quantized Control

Junjian Huang, Junren Wang, Yao Tan
Asian Journal of Control
Neural Networks Stability and Synchronization
article

Finite‐Time and Fixed‐Time Synchronization of Memristor‐based Neural Networks With Proportional Delays via Chattering‐free Quantized Control

Junjian Huang, Junren Wang, Yao Tan
article en

Abstract

ABSTRACT The study designed two chattering‐free quantized controllers aimed at achieving finite‐time and fixed‐time synchronization of memristive neural networks (MNNs) based on quantized control. These controllers are not only easy to implement but also effectively handle the mismatched uncertainties and proportional delays in Filippov solutions, ensuring chattering‐free operation. By introducing a new Lyapunov function and using the 1‐norm method, the study derived several sufficient conditions to guarantee drive‐response synchronization of MNNs within finite or fixed time. To verify the accuracy of these theoretical results, numerical simulations were conducted, demonstrating the effectiveness and reliability of the proposed methods.

Asian Journal of Control
Southwest University (CN)
National Natural Science Foundation of China, Chongqing Municipal Education Commission
Openalex Percentile: Top 12%
Neural Networks Stability and Synchronization
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