Global Exponential Stability of Fractional Order Complex-Valued Neural Networks with Leakage Delay and Mixed Time Varying Delays

被引：13

作者：

Hymavathi, M. ^{[1
]}

Muhiuddin, G. ^{[2
]}

Ali, M. Syed ^{[1
]}

Al-Amri, Jehad F. ^{[3
]}

Gunasekaran, Nallappan ^{[4
]}

Vadivel, R. ^{[5
]}

机构：

[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India

[2] Univ Tabuk, Fac Sci, Dept Math, POB 741, Tabuk 71491, Saudi Arabia

[3] Taif Univ, Coll Comp & Informat Technol, Dept Informat Technol, POB 11099, At Taif 21944, Saudi Arabia

[4] Toyota Technol Inst, Computat Intelligence Lab, Nagoya, Aichi 4688511, Japan

[5] Phuket Rajabhat Univ, Fac Sci & Technol, Dept Math, Phuket 83000, Thailand

来源：

FRACTAL AND FRACTIONAL | 2022年 / 6卷 / 03期

关键词：

complex-valued neural networks; global exponential stability; linear matrix inequality; UNIFORM STABILITY; SYNCHRONIZATION; BIFURCATION; DISCRETE;

D O I：

10.3390/fractalfract6030140

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper investigates the global exponential stability of fractional order complex-valued neural networks with leakage delay and mixed time varying delays. By constructing a proper Lyapunov-functional we established sufficient conditions to ensure global exponential stability of the fractional order complex-valued neural networks. The stability conditions are established in terms of linear matrix inequalities. Finally, two numerical examples are given to illustrate the effectiveness of the obtained results.

引用

页数：20

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