Dynamical System in Chaotic Neurons with Time Delay Self‐Feedback and Its Application in Color Image Encryption

Abstract

The time delay caused by transmission in neurons is often ignored, but it is demonstrated by theories and practices that time delay is unavoidable. A new chaotic neuron model with time delay self-feedback is proposed based on Chen’s chaotic neuron. The bifurcation diagram and Lyapunov exponential diagram are used to analyze the chaotic characteristics of neurons in the model when they receive the output signals at different times. The experimental results exhibit that it has a rich dynamic behavior. In addition, the randomness of chaotic series generated by chaotic neurons with time delay self-feedback under different conditions is verified. In order to investigate the application of this model in image encryption, an image encryption scheme is proposed. The security analysis of the simulation results shows that the encryption algorithm has an excellent anti-attack ability. Therefore, it is necessary and practical to study chaotic neurons with time delay self-feedback.

The Effect of Blue Noise on the Optimization Ability of Hopfield Neural Network

“…Hopfield is a feedback neural network model (HNN, Hopfield neural network), which is mainly used to solve various optimization problems [3,4] and also has a wide range of uses in real-world applications, such as image processing [5][6][7], predictive classification [8][9][10], and communication [11,12], and Hopfield neural networks are prone to generating random noise due to their physical properties. Based on this common phenomenon in real-world applications, the paper introduces blue noise into Hopfield neural networks to simulate the noise in real-world applications.…”

Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling

“…Yang et al introduced the Hankel Alternative View of Koopman analysis to decompose chaotic dynamics into a linear model with intermittent forcing [15]. The most widely used methods of chaotic time series analysis are neural network-related methods, which are classified into artificial neural networks (ANN) [16][17][18][19][20], fuzzy neural networks (FNN) [21][22][23][24], optimization algorithms with ANN [25][26][27][28], and wavelet neural networks (WNN) [29][30][31][32]. One can refer to [33] for a comprehensive review.…”