Reachable Set Estimation of Memristive Inertial Neural Networks

IF 4.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Circuits and Systems II: Express Briefs Pub Date : 2025-03-23 DOI:10.1109/TCSII.2025.3572855

Yuxin Jiang;Song Zhu;Shiping Wen;Chaoxu Mu

引用次数: 0

Abstract

This brief investigates the reachable set estimation (RSE) issues for delayed memristive inertial neural networks (MINNs) subject to bounded disturbances. By employing nonreduced-order method and reduced-order method, novel algebraic conditions are derived to estimate the reachable sets of the considered MINNs. The analysis reveals that each method is applicable under different conditions. In contrast to the previous RSE results, the proposed methods yield tighter bounded estimations. Finally, a comparative experiment is conducted to verify the corresponding findings.

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记忆性惯性神经网络的可达集估计

摘要研究了受有界扰动影响的延迟记忆性惯性神经网络的可达集估计问题。通过采用非降阶方法和降阶方法，推导了新的代数条件来估计所考虑的最小神经网络的可达集。分析表明，每种方法适用于不同的条件。与以前的RSE结果相比，所提出的方法产生更严格的有界估计。最后通过对比实验验证了相应的研究结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Circuits and Systems II: Express Briefs 工程技术-工程：电子与电气

CiteScore

7.90

自引率

20.50%

发文量

883

审稿时长

3.0 months

期刊介绍： TCAS II publishes brief papers in the field specified by the theory, analysis, design, and practical implementations of circuits, and the application of circuit techniques to systems and to signal processing. Included is the whole spectrum from basic scientific theory to industrial applications. The field of interest covered includes: Circuits: Analog, Digital and Mixed Signal Circuits and Systems Nonlinear Circuits and Systems, Integrated Sensors, MEMS and Systems on Chip, Nanoscale Circuits and Systems, Optoelectronic Circuits and Systems, Power Electronics and Systems Software for Analog-and-Logic Circuits and Systems Control aspects of Circuits and Systems.