新型忆阻细胞神经网络的建模及电路仿真

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (8): 1715-1725.

新型忆阻细胞神经网络的建模及电路仿真

张小红, 廖琳玉, 俞梁华

江西理工大学信息工程学院,赣州 341000

收稿日期:2015-02-03 修回日期:2015-05-03 出版日期:2016-08-08 发布日期:2020-08-17
作者简介:张小红(1966-),女,河北昌黎,博士,教授,研究方向为非线性动力学,视频保密通信。
基金资助:
国家自然科学基金(11062002, 61363076),江西省自然基金(20142BAB207020),江西省教育厅科技项目(GJJ150621, GJJ14439), 江西省研究生创新专项资金(YC2014-S368)

Novel Modeling of Memristive Cellular Neural Network and Its Circuit Simulation

Zhang Xiaohong, Liao Linyu, Yu Lianghua

School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China

Received:2015-02-03 Revised:2015-05-03 Online:2016-08-08 Published:2020-08-17

摘要/Abstract

摘要： 作为继电阻、电容、电感三大电路基本元件外的第四类电路元件忆阻器,可模拟大脑突触神经网络的存储记忆功能。构建一个传统三维细胞神经网络,利用磁控忆阻器的非线性特性替换传统细胞神经网络的输出模块。采用Multisim通用电路元件构建磁控忆阻等效电路,在电路整体设计上简化了输出函数模块数量,与具有混沌行为的CNN系统相比,新型忆阻CNN电路不仅展现出了混沌吸引子现象,而且忆阻内部的磁能量随细胞状态而变化,可完全达到等效输出函数的忆导值。数值计算与电路仿真结果验证了忆阻细胞神经网络的混沌特性及新设计方法的可行性,在信号处理、同步控制与图像加密等方面具有现实的应用价值。

关键词: 细胞神经网络, 忆阻器, 混沌, 分段线性, 电路仿真, 保密通信

Abstract: The memristor as the fourth fundamental circuit component included along with the resistor, capacitor and inductor could simulate the storage memory function of brain neural network. A traditional three-dimensional CNN was designed, and the output function was implemented with the nonlinear characteristics of a flux-controlled memristor. The conventional electronic devices in Multisim were utilized to simulate flux-controlled memristor which simplified the output function part of the circuits. To compare with the CNN system which has chaotic behaviors, the circuits of novel memristive cellular neural network not only can show a chaos attractors phenomenon, but also the inner magnetic energy of memristor is changed with state of CNN to achieve the equivalent values of output function. Numerical calculation and circuit simulation results verify its chaotic characteristics and feasibility, and can be applied in synchronization communication, image encryption and speech signal processing, et al.

Key words: cellular neural network, memristor, chaotic, piecewise-linear, circuit simulation, secure communication

中图分类号:

TP391.9/O415.5

张小红, 廖琳玉, 俞梁华. 新型忆阻细胞神经网络的建模及电路仿真[J]. 系统仿真学报, 2016, 28(8): 1715-1725.

Zhang Xiaohong, Liao Linyu, Yu Lianghua. Novel Modeling of Memristive Cellular Neural Network and Its Circuit Simulation[J]. Journal of System Simulation, 2016, 28(8): 1715-1725.

参考文献 21

[1]	Chua L O.Memristor-The Missing Circuit Element[J]. IEEE Transaction on Circuit Theory (S0018-9324), 1971, 18(5): 507-519.
[2]	Strukov D B, Snider G S, Stewart G R, et al.The missing memristor found[J]. Nature (S0028-0836), 2008, 453: 80-83.
[3]	Biolek Z, Biolek D, Biolkva V.SPICE model of memristor with nonlinear dopant drift[J]. Radio engineering (S0013-5194), 2009, 18(2): 210-214.
[4]	Wang X, Chen Y, Xi H, Dimitrov D.Spintronic memristor through spin-torque-induced magnetization motion[J]. IEEE Electron Device Letters (S0741-3106), 2009, 30(3): 294-297.
[5]	Peishin Y V, Ventra M D.Memory effects in complex materials and nanoscale systems[J]. Advances in Physics (S0001-8732), 2011, 60(2): 145-227.
[6]	胡丰伟, 包伯成, 武花干, 等. 荷控忆阻等效电路分析模型及其电路特性研究[J]. 物理学报, 2013, 62(21): 218401.
[7]	洪庆辉, 曾以成, 李志军. 含磁控和荷控两种忆阻器的混沌电路设计与仿真[J]. 物理学报, 2013, 62(23): 230502.
[8]	Muthuswamy B, Kokate P P.Memristor Based Chaotic Circuits[J]. IETE Technical Review (S0256-4602), 2009, 26(6): 417-429.
[9]	Chua L O, Yang L.Cellular Neural Networks: theory[J]. IEEE Transactions on Circuits and systems (S1549-8328), 1988, 35(10): 1257-1272.
[10]	Chua L O, Yang L.Celluar Neural Networks: Application[J]. IEEE Transactions on Circuits and Systems (S1549-8328), 1988, 35(10): 1273-1290.
[11]	冯强, 于盛林, 张维. 基于细胞神经网络的图像复原新方法[J]. 中国图像图形学报, 2009, 14(3): 430-434.
[12]	Nakayama Y, Hosokawa Y, Nishio Y.Possibility of image processing by CNN using two kinds of cloning templates[C]// IEEE Workshop on Nonlinear Circuit Networks. USA: IEEE, 2013: 81-84.
[13]	Arun P V, Katiyar S K.Cellular neural network-based hybrid approach toward automatic image registration[J]. Journal of Applied Remote Sensing (S1931-3195), 2013, 7(1): 1-11.
[14]	Elango P, Murugesan K.Digital Image Inpainting Using Cellural Neural Network[J]. Int. J. Open Problems Compt. Math (S1998-6262), 2009, 2(3): 439-450.
[15]	Swathy P S, Sabarathinam S, Suresh K, et al.Chaos synchronization and transmission of information in coupled SC-CNN-based canonical Chua’s circuit[J]. Nonliear Dynamics (S0924-090X), 2014, 78(2): 1033-1047.
[16]	张文娟, 康家银. 一种用于轮廓线探测的CNN改进算法[J]. 系统仿真学报, 2012, 24(8): 1629-1632.(Zhang Wenjuan, Kang Jiayin.Modified CNN Algorithm for Contour Detection[J]. Journal of System Simulation (S1004-731X), 2012, 24(8): 1629-1632.)
[17]	Yang L, Chua L O, Krieg K R.VLSI Implementation of Cellular Neural Networks[J]. IEEE International Symposium on Circuits and Systems (S0271-4310), 1990, 384(3): 2425-2427.
[18]	Lehtonen E, Laiho M.CNN using memristor for neighborhood connections[C]// 2010 12th International workshop on cellular nanoscale networks and their application (CNNA). Berkeley, California, USA. 2010: 1-4.
[19]	Itoh M, Chua L O.Memristor Cellular Automata and Memristor Discrete-Time Cellular Neural Networks[J]. International Journal of Bifurcation and Chaos (S0218-1274), 2009, 19(11): 3605-3656.
[20]	黄立宏, 李雪梅. 细胞神经网络动力学 [M]. 北京: 科学出版社, 2007.
[21]	李志军, 曾以成, 李志斌. 改进型细胞神经网络实现的忆阻器混沌电路[J]. 物理学报, 2014, 63(1): 010502.