Circuit design and image encryption of CNN chaotic system based on memristor

• Published in: The European physical journal. B, Condensed matter physics Condensed matter physics, 2024-07, Vol.97 (7), Article 100
• Main Authors: Zhang, Jie, Liu, Enze
• Format: Article
• Language: English
• Subjects: Analog circuits; Artificial neural networks; Bifurcations; Chaos theory; Circuit design; Circuit diagrams; Complex Systems; Complexity; Condensed Matter Physics; Digital electronics; Digital imaging; Dynamic characteristics; Entropy (Information theory); Field programmable gate arrays; Fluid- and Aerodynamics; Liapunov exponents; Memristors; Physics; Physics and Astronomy; Regular Article - Statistical and Nonlinear Physics; Solid State Physics; Wavelet analysis
• ISSN: 1434-6028; 1434-6036
• DOI: 10.1140/epjb/s10051-024-00743-y

A new general-purpose voltage-controlled memristor with a fourth power term is designed in this paper. Based on this memristor, a new four-dimensional chaotic system, the memristor cell neural network (CNN) chaotic system, is constructed with complex dynamic characteristics, high spectral entropy complexity (SE), and wavelet entropy complexity ( C 0 ). Combination with analysis including phase portraits, equilibrium points, dissipativity, Lyapunov exponent spectrum, bifurcation diagrams, transient phenomena, spectral entropy complexity (SE), and wavelet entropy complexity ( C 0 ), the dynamic characteristics of the new system are thoroughly explored. Constructed analog circuits for the new memristor and new memristor CNN chaotic system, and a digital circuit is implemented using a field-programmable gate array. Combining the new memristor CNN chaotic system with DNA encoding operations for image encryption applications, its excellent encryption effectiveness and high security are discovered through analysis of key sensitivity, histogram, information entropy, correlation, keyspace, and robustness. Graphical abstract