控制理论(社会学)
人工神经网络
一般化
理论(学习稳定性)
计算机科学
趋同(经济学)
传输(电信)
事件(粒子物理)
李雅普诺夫函数
国家(计算机科学)
控制(管理)
Lyapunov稳定性
数学
算法
人工智能
电信
非线性系统
量子力学
经济增长
机器学习
物理
数学分析
经济
作者
Jiashu Gao,Jing Han,Guodong Zhang
出处
期刊:AIMS mathematics
[American Institute of Mathematical Sciences]
日期:2024-01-01
卷期号:9 (4): 9211-9231
被引量:2
摘要
<abstract><p>This study employed an event-triggered control (ETC) strategy to investigate the problems of fixed-time stabilization (FTS) and preassigned-time stabilization (PTS) for state-dependent switching neural networks (SDSNNs) that involved mixed time delays. To enhance the network's generalization capability and accelerate convergence stabilization, a more intricate weight-switching mechanism was introduced, then to mitigate transmission energy consumption, this paper proposed a tailored event-triggering rule that triggered the ETC solely at predetermined time points. This rule ensured the stability of the system while effectively reducing energy consumption. Using the Lyapunov stability theory and various inequality techniques, this paper presented new results for FTS and PTS of SDSNNs. The validity of these findings was supported by conducting data simulations in two illustrative examples.</p></abstract>
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