Exponential Input-To-State Stability of Quaternion-Valued Memristive Neural Networks: Continuous and Discrete Cases

被引：1

作者：

Li, Ruoxia ^{[1
]}

Cao, Jinde ^{[2
,3
]}

Abdel-Aty, Mahmoud ^{[4
,5
]}

机构：

[1] Shaanxi Normal Univ, Sch Math & Stat, Xian, Peoples R China

[2] Southeast Univ, Sch Math, Nanjing, Peoples R China

[3] Ahlia Univ, Manama, Bahrain

[4] Ahlia Univ, Deanship Grad Studies & Sci Res, Manama, Bahrain

[5] Sohag Univ, Fac Sci, Math Dept, Sohag, Egypt

来源：

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2025年 / 39卷 / 02期

关键词：

input-to-state stability; memristive system; norm; quaternion continuous and discrete; DELAY; SYNCHRONIZATION; STABILIZATION;

D O I：

10.1002/acs.3943

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article focuses on the input-to-state stability (ISS) issue of quaternion-valued memristive networks. Employing the quaternion norm tool and the Lyapunov method, two improved conclusions are developed for the continuous networks. After that, via the semidiscretization technique, a new discrete model is designed, and its ISS performance is discussed and subsequently recur to a nonlinear scalarization approach. Less conservative results are obtained since the nonlinear scalarization approach makes the quaternion interval meaningful. Simulations are presented to verify the validity of the outcomes.

引用

页码：372 / 382

页数：11

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