Deep Adaptive Chaos Synchronization Based on Optimization Algorithm

In this study, we propose a novel Deeply Optimized Adaptive chaotic synchronization algorithm system (DOA), which adopts the ideas of genetic algorithm, Deep Image Prior (DIP) network, Deep Convolutional Generative Adversarial Network (DCGAN) network, and slide mode control algorithm. Traditional Deep Learning (DL)-based methods perform well in complex multi-parameter operations, but training on large datasets is typically a complicated, time-consuming, and high-cost process. Such methods are also difficult to adapt to dynamic parameter changes. The algorithmic network model in the proposed DOA reduces reliance on large datasets by learning the deep mining methods of the data characteristics in DIP, and can adjust system parameters adaptively, accurately, and quickly, providing high synchronization efficiency and excellent stability over various chaotic signals. By applying Lyapunov stability theory, the robustness and global stability of the model in dynamic systems are proven. This paper also uses an advanced Recurrent Neural Network (RNN)-based chaotic synchronization system as a benchmark. The simulation results show that, when compared to the Recurrent Neural Network based synchronization system, the DOA architecture has significant advantages in robustness, convergence, and training over noisy channels. Experiments show that under strong noise (AWGN variance = 2) and parameter mismatch (±20 percent drift), the synchronization error of DOA (<0.3) is significantly lower than that of the RNN benchmark system (>1.5), and the training data volume is reduced by more than 30%. Simulation results show that, the DOA architecture has significant advantages in robustness, convergence, and training over noisy channels. The proposed DOA scheme improves the effect of chaotic synchronization and paves the way for the development of a new class of modulator schemes that meet the robustness, convergence, and training requirements for encrypted communication.