Dynamic analysis and application in medical digital image watermarking of a new multi-scroll neural network with quartic nonlinear memristor

Abstract

Memristor is widely used in various neural bionic models because of its excellent characteristics in biological neural activity simulation. In this paper, a piecewise nonlinear function is used to transform the quartic memristor, which is introduced into the ternary Hopfield neural network (HNN) with self-feedback, and a piecewise quartic memristive chaotic neural network model with multi-scroll is constructed. Through simulation analysis, the number of scroll layers changes with memristor parameters and has significant coexistence of multi-scroll attractors and high initial value sensitivity has been found. Using its excellent unpredictability, a digital watermarking algorithm based on wavelet transform is improved and used in the protection of personal medical data. The results show that it not only improves the confidentiality and convenience, but also ensures its robustness and has good encryption effect.

Dynamic Analysis and Audio Encryption Application in IoT of a Multi-Scroll Fractional-Order Memristive Hopfield Neural Network

“…Due to the introduction of chaotic dynamics, CNN has more dynamic characteristics. The complex dynamic characteristics of CNN make it widely used in information processing, algorithm optimization, image encryption and random number generators [17][18][19][20][21], etc.…”

Four-scroll attractor on the dynamics of a novel Hopfield neural network based on bi-neurons without bias current

“…Based on the brain nervous topology, a simplified brain neural network was constructed by Hopfield in 1982 [1] . Recently Hopfield neural network have been investigated with different topology who is able to generate complex phenomena with a simplest configuration chaos [2] , [3] , [4] , hyperchaos [5] , [6] , multi-scroll [7] , [8] , antimonotonicity [9] and bursting oscillation [10] to name a few. Memristive synapse can be used to emulate the electromagnetic induction effects.…”

Asymptotic Synchronization of Memristive Cohen-Grossberg Neural Networks with Time-Varying Delays via Event-Triggered Control Scheme

“…Based on the excellent characteristics of memristor [ 6 , 7 , 8 ], a variety of chaotic circuits and systems based on memristor are proposed. Memristive neural network (MNN), which simulates synaptic connection with memristor, has attracted much attention owing to its application in logic operation and image processing [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”