Reachable Set Estimation of Inertial Complex-Valued Memristive Neural Networks

IF 4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Circuits and Systems II: Express Briefs Pub Date : 2024-10-28 DOI:10.1109/TCSII.2024.3486746

Jiemei Zhao;Yi Shen;Leimin Wang;Liqi Yu

引用次数: 0

Abstract

This brief investigates the reachable set estimation (RSE) of inertial complex-valued memristive neural networks (ICVMNNs) with bounded disturbances. By taking into account the analysis method and inequality technique, an algebraic criterion of RES is established. To deal with the inertial terms in memristive neural networks, a nonreduced-order approach is adopted. Besides, the non-separation analysis method is applied to investigate complex-valued problems. Then, a complex-valued feedback control scheme is designed to ensure that the states of ICVMNNs converge to a bounded region. Eventually, a numerical example is provided to illustrate the effectiveness of the obtained theoretical result.

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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.