计算机科学
人工神经网络
现场可编程门阵列
加密
像素
Hopfield网络
混乱的
记忆电阻器
偏移量(计算机科学)
伪随机数发生器
计算机硬件
算法
拓扑(电路)
电子工程
人工智能
电气工程
工程类
操作系统
程序设计语言
作者
Fei Yu,Xinxin Kong,Abdulmajeed Abdullah Mohammed Mokbel,Wei Yao,Shuo Cai
出处
期刊:IEEE Transactions on Circuits and Systems Ii-express Briefs
[Institute of Electrical and Electronics Engineers]
日期:2022-11-04
卷期号:70 (1): 326-330
被引量:84
标识
DOI:10.1109/tcsii.2022.3218468
摘要
Because of the nonlinearity and memory, memristors are the most suitable electrical component for simulating synapses. A novel local active and nonvolatile memristor is designed. By circuit experiments, its memristive properties are verified. By introducing this memristor, this brief constructs a 4D memristive Hopfield neural network (MHNN) which can perform complex dynamics, such as controllable double-scrolls attractors and controllable initial offset boosting coexistence. Compared with other multiscroll chaotic systems, the autonomy equation of the system is smooth for discarding the sign function. In addition, this MHNN performs well in image encryption applications for the significant complexity of multiscroll. Through safety analysis, the information entropy of the $512 \times 512$ Lena graph is 7.9993, which is very close to the ideal value of 8. Besides, the number of pixels changing rates (NPCR) and the unified averaged changed intensity (UACI) are 99.6097% and 33.4621%, which are almost equal ideal values. Finally, this brief designs the digital circuit of the multiscroll MHNN signal generator and verifies the function with the help of a field programmable gate array (FPGA) and oscilloscope. Besides, by designing a pseudo-random number generation circuit, FPGA can directly encrypt the image and transmit it to the input and output devices.
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