Scholarlink

<p>This research focuses on the exponential synchronization of uncertain complex-valued inertial neural networks (CVINNs) with time-varying delays (TVDs) and distributed TVDs. Firstly, considering the fragility of controllers and transmission delays, a non-fragile memory sampled-data control (SDC) approach is employed to address the synchronization issue of CVINNs. Subsequently, to further reduce conservatism, the improved reciprocally convex inequality is extended to the complex domain. Furthermore, a Lyapunov-Krasovskii functional (LKF) based on the entire sampling period is designed, and combined with the improved complex-valued inequalities, sufficient conditions for the exponential synchronization of the addressed system are derived. Finally, a numerical simulation and an application example are developed, aiming to validate both theoretical efficacy and implementation feasibility.</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

Non-fragile memory sampled-data control for exponential synchronization of uncertain complex-valued inertial neural networks with mixed delays

  • 期刊论文

  • 发表日期：

    2025年7月22日

查看全文
  • Ziye Zhang,
  • Ping Sun,
  • Runan Guo,
  • Zhen Wang,
  • Chong Lin

摘要

This research focuses on the exponential synchronization of uncertain complex-valued inertial neural networks (CVINNs) with time-varying delays (TVDs) and distributed TVDs. Firstly, considering the fragility of controllers and transmission delays, a non-fragile memory sampled-data control (SDC) approach is employed to address the synchronization issue of CVINNs. Subsequently, to further reduce conservatism, the improved reciprocally convex inequality is extended to the complex domain. Furthermore, a Lyapunov-Krasovskii functional (LKF) based on the entire sampling period is designed, and combined with the improved complex-valued inequalities, sufficient conditions for the exponential synchronization of the addressed system are derived. Finally, a numerical simulation and an application example are developed, aiming to validate both theoretical efficacy and implementation feasibility.