Scholarlink

<p>This study addresses finite-time synchronization(FTS) of quaternion-valued neural networks (QVNNs) under time-varying and proportional delays. To achieve synchronization, a novel quantized sampled-data hybrid controller (QSDHC) is developed. By establishing a necessary Lyapunov-Krasovskii functional (LKF) and employing advanced inequality tactics, adequate synchronization criteria are derived by means of linear matrix inequalities (LMIs). Furthermore, an optimal algorithm is introduced to approximate the settling time, ensuring finite-time convergence of the error system. Numerical experiments validate the theoretical findings, while an application to image recovery emphasizes the validity of the presented technique, showcasing the potential of QVNNs in handling multidimensional data processing tasks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Finite-Time Synchronization of Quaternion-Valued Neural Networks with Time-Varying and Proportional Delay under Hybrid Sampled-Data Control with Application to Secure Image Encryption

  • 期刊论文

  • 发表日期：

    2026年2月13日

查看全文
  • Jincy Jacob,
  • S Dharani

摘要

This study addresses finite-time synchronization(FTS) of quaternion-valued neural networks (QVNNs) under time-varying and proportional delays. To achieve synchronization, a novel quantized sampled-data hybrid controller (QSDHC) is developed. By establishing a necessary Lyapunov-Krasovskii functional (LKF) and employing advanced inequality tactics, adequate synchronization criteria are derived by means of linear matrix inequalities (LMIs). Furthermore, an optimal algorithm is introduced to approximate the settling time, ensuring finite-time convergence of the error system. Numerical experiments validate the theoretical findings, while an application to image recovery emphasizes the validity of the presented technique, showcasing the potential of QVNNs in handling multidimensional data processing tasks.