Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays

被引：5
作者：
Yao, Luogen [1 ,2 ]
Cao, Qian [3 ]
机构：
[1] Hunan Univ Technol & Business, Key Lab Hunan Prov Stat Learning & Intelligent Co, Changsha 410205, Hunan, Peoples R China
[2] Hunan Univ Technol & Business, Sch Math & Stat, Changsha 410205, Hunan, Peoples R China
[3] Hunan Univ Arts & Sci, Coll Math & Phys, Changde 415000, Hunan, Peoples R China
来源：
JOURNAL OF INEQUALITIES AND APPLICATIONS | 2020年 / 2020卷 / 01期
关键词：
High-order inertial neural networks; Anti-periodic solution; Global exponential stability; Mixed delay; NICHOLSONS BLOWFLIES MODEL; EXPONENTIAL STABILITY; ANTIPERIODIC SOLUTIONS; LIMIT-CYCLES; FORMS GRAPHS; DYNAMICS; EXISTENCE; EQUATIONS; ENDOMORPHISMS; SYSTEMS;
D O I：
10.1186/s13660-020-02444-3
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper deals with a class of high-order inertial Hopfield neural networks involving mixed delays. Utilizing differential inequality techniques and the Lyapunov function method, we obtain a sufficient assertion to ensure the existence and global exponential stability of anti-periodic solutions of the proposed networks. Moreover, an example with a numerical simulation is furnished to illustrate the effectiveness and feasibility of the theoretical results.
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页数：22
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