首页|Exponential stability and periodic solutions of delayed cellular neural networks

Exponential stability and periodic solutions of delayed cellular neural networks

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A set of criteria are presented for the global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov functionals, introducing many parameters qij, rij, qij*, rij*∈R and wi＞0 (i, j=1, 2,…,n) and combining them with the elementary inequality 2ab≤a2+b2 technique. These criteria have important significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. In addition, the results in literature are extended and improved. Two examples are given to illustrate the theory.

periodic solutionglobal exponential stabilitydelayed cellular neural networksLyapunov functionalinequality

CAO Jinde

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Department of Applied Mathematics, Southeast University, Nanjing 210096, China

Adult Education College, Yunnan University, Kunming 650091, China

Natural Science Foundation of Yunnan Province, ChinaNatural Science Foundation of Yunnan Province, China

1999F0017M,97A012G96A001M

2000

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

影响因子：1.056
ISSN：1674-7321
年,卷(期)：2000.43(3)
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