Complex dynamic behaviors in hyperbolic-type memristor-based cellular neural network

作     者：Ai-Xue Qi Bin-Da Zhu Guang-Yi Wang 齐爱学;朱斌达;王光义

作者机构：Faculty of Aerospace EngineeringBinzhou UniversityBinzhou 256603China Institute of Modern Circuits and Intelligent InformationHangzhou Dianzi UniversityHangzhou 310018China 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2022年第31卷第2期

页      面：255-268页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 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）] 

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.61771176 and 62171173) 

主　　题：memristor cellular neural network chaos 

摘      要：This paper presents a new hyperbolic-type memristor model,whose frequency-dependent pinched hysteresis loops and equivalent circuit are tested by numerical simulations and analog integrated operational amplifier *** on the hyperbolic-type memristor model,we design a cellular neural network(CNN)with 3-neurons,whose characteristics are analyzed by bifurcations,basins of attraction,complexity analysis,and circuit *** find that the memristive CNN can exhibit some complex dynamic behaviors,including multi-equilibrium points,state-dependent bifurcations,various coexisting chaotic and periodic attractors,and offset of the positions of *** calculating the complexity of the memristor-based CNN system through the spectral entropy(SE)analysis,it can be seen that the complexity curve is consistent with the Lyapunov exponent spectrum,i.e.,when the system is in the chaotic state,its SE complexity is higher,while when the system is in the periodic state,its SE complexity is ***,the realizability and chaotic characteristics of the memristive CNN system are verified by an analog circuit simulation experiment.