Novel Results on Fixed-Time Complex Projective Lag Synchronization for Fuzzy Complex-Valued Neural Networks With Inertial Item

被引：0
|
作者：
Yao, Yu [1 ]
Han, Jing [2 ]
Zhang, Guodong [1 ]
Hu, Junhao [1 ]
机构：
[1] South Cent Minzu Univ, Coll Math & Stat, Wuhan 430074, Peoples R China
[2] Wuhan Business Univ, Sch Informat Engn, Wuhan 430000, Peoples R China
来源：
IEEE ACCESS | 2024年 / 12卷
基金：
中国国家自然科学基金;
关键词：
Synchronization; Artificial neural networks; Numerical stability; Pattern recognition; Fuzzy control; Adaptive control; Fixed-time complex projective lag synchronization (FXCPLS); fuzzy neural networks; complex-valued neural networks; inertial item; FINITE-TIME; STABILIZATION; STABILITY; DESIGN;
D O I：
10.1109/ACCESS.2024.3413785
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 paper focuses on investigating fuzzy complex-valued neural networks with inertial item. By utilizing fixed-time stability theory and inequality techniques, we designed two types of feedback controllers and obtained some new criteria to ensure that the system achieves fixed-time complex projective lag synchronization(CPLS). Compared with previous works, we study the complex-value system as a whole, and compared to ordinary synchronization, CPLS has a broader range of applications. Finally, we provide numerical simulations to verify the effectiveness of the theoretical results.
引用
收藏
页码：86120 / 86131
页数：12
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