Complex Dynamics, Hardware Implementation and Image Encryption Application of Multiscroll Memeristive Hopfield Neural Network With a Novel Local Active Memeristor

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 x 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.

Automated summary

A novel local active and nonvolatile memristor is designed and its memristive properties are verified through circuit experiments. A 4D memristive Hopfield neural network (MHNN) is constructed using this memristor, which can perform complex dynamics and is suitable for image encryption applications.