计算智能
人工神经网络
控制器(灌溉)
理论(学习稳定性)
扩散
反应扩散系统
控制理论(社会学)
复杂系统
事件(粒子物理)
计算机科学
数学
物理
人工智能
控制(管理)
数学分析
机器学习
热力学
生物
量子力学
农学
作者
Huiyu Wang,Shutang Liu,Xiang Wu,Jie Sun,Wei Qiao
标识
DOI:10.1007/s11063-024-11509-z
摘要
Abstract This article explores the asymptotic stability of fractional delayed memristive neural networks with reaction-diffusion terms. A novel hybrid impulsive controller triggered by a specific event is proposed to stabilize the network, thereby replacing the conventional approach of modifying network parameters. The proposed controller is proven to prevent Zeno behavior. Sufficient conditions for the asymptotic stability of fractional delayed memristive neural networks with reaction-diffusion terms are established through Lyapunov direct method, inequality techniques, Green’s theorem and impulse analysis. Furthermore, the proposed controller is theoretically shown to be more resource-efficient than the conventional one, and our work extends existing research to make it more suitable for practical application such as pattern recognition, image processing and so on. Finally, an example is provided to illustrate the validity of the findings.
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