Exponential stabilization for delayed memristive neural networks by comparison strategy | Zendy

Fenglin Wang | Zendy; Jiemei Zhao | Zendy; Ning Wu | Zendy; Yaqin Li | Zendy

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Exponential stabilization for delayed memristive neural networks by comparison strategy

Author(s) -

Fenglin Wang,

Jiemei Zhao,

Ning Wu,

Yaqin Li

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3571786

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This article is concerned with the r th moment global exponential stabilization of delayed memristive neural networks (DMNNs). By using the comparison strategy, the theories of differential inclusion and inequality techniques, the exponential stabilization of the DMNNs is investigated. To achieve this purpose, a state feedback controller and an adaptive controller are designed, respectively. The comparison strategy is a new analytical method without employing Lyapunov stability theory and relaxes the constraint of time delays. In addition, the obtained results are represented by algebraic criteria, which are convenient for testing. In the end, a numerical simulation is given to show the validity of the derived criteria.

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