Scholarlink

<p>In this paper, we introduce a new performance index that examines the extended dissipative criteria in a finite-time contractive boundedness framework for the delayed discrete-time neural networks (DT-NNs). Initially, to avoid unnecessary resource consumption and to govern the information broadcast, the network-induced delay-dependent event-triggered state estimation approach is employed for the considered DT-NNs. In addition, a new discrete inequality for the single summable term is derived based on the generalized free-weighting-matrix inequality and parameter-dependent reciprocally convex inequality. Novel delay-square dependent Lyapunov-based sufficient conditions are employed to obtain the enhanced finite-time extended dissipative performance based on derived summation inequality. Furthermore, an example is provided in both numerical and simulation domains to exemplify the effectiveness of the proposed theoretical approach.</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

Extended dissipativity-based finite-time contractive boundedness for delayed discrete-time neural networks via event-triggered approach

  • 期刊论文

  • 发表日期：

    2025年7月18日

查看全文
  • B. Adhira,
  • G. Nagamani

摘要

In this paper, we introduce a new performance index that examines the extended dissipative criteria in a finite-time contractive boundedness framework for the delayed discrete-time neural networks (DT-NNs). Initially, to avoid unnecessary resource consumption and to govern the information broadcast, the network-induced delay-dependent event-triggered state estimation approach is employed for the considered DT-NNs. In addition, a new discrete inequality for the single summable term is derived based on the generalized free-weighting-matrix inequality and parameter-dependent reciprocally convex inequality. Novel delay-square dependent Lyapunov-based sufficient conditions are employed to obtain the enhanced finite-time extended dissipative performance based on derived summation inequality. Furthermore, an example is provided in both numerical and simulation domains to exemplify the effectiveness of the proposed theoretical approach.