DYNAMICS OF NEW CLASS OF HOPFIELD NEURAL NETWORKS WITH TIME-VARYING AND DISTRIBUTED DELAYS

作     者：Adnene ARBI Farouk CHERIF Chaouki AOUITI Abderrahmen TOUATI 

作者机构：Department of MathematicsFacultd des Sciences de BizerteUniversity of CarthageJarzouna 7021BizerteTunisia Department of Computer ScienceISSATSLaboratory of Math PhysicsUniversity of SousseSpecials Functions and Applications LR11ES35ecole suprieure des sciences et de technologieSousse 4002Tunisia 

出 版 物：《Acta Mathematica Scientia》 (数学物理学报（B辑英文版）)

年 卷 期：2016年第36卷第3期

页      面：891-912页

核心收录：

学科分类：0711[理学-系统科学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：delayed functional differential equations neural networks pseudo-almost peri- odic solution global exponential stability time-varying and distributed delays fixed point theorem 

摘      要：In this paper, we investigate the dynamics and the global exponential stability of a new class of Hopfield neural network with time-varying and distributed delays. In fact, the properties of norms and the contraction principle are adjusted to ensure the existence as well as the uniqueness of the pseudo almost periodic solution, which is also its derivative pseudo almost periodic. This results are without resorting to the theory of exponential dichotomy. Furthermore, by employing the suitable Lyapunov function, some delayindependent sufficient conditions are derived for exponential convergence. The main originality lies in the fact that spaces considered in this paper generalize the notion of periodicity and almost periodicity. Lastly, two examples are given to demonstrate the validity of the proposed theoretical results.