A time-delay signature elimination and broadband electro-optic chaotic system with enhanced nonlinearity by deep learning

Abstract

In this paper, a novel electro-optic chaotic system with enhanced nonlinearity by deep learning (ENDL) is proposed to achieve time-delay signature (TDS) elimination. A long-short term memory network (LSTM) is trained by a specially designed loss function to enhance the nonlinear effect that can hide the TDS of the system. For the first time, the trained deep learning module is put into a single feedback loop to participate in chaos generation. Simulation results show that the ENDL system can eliminate TDS and increase the bandwidth to more than 31GHz when the feedback intensity is very low (α = 4V). Moreover, the complexity of the chaotic output can be improved with permutation entropy (PE) reaching 0.9941. The synchronization result shows that the ENDL system has high sensitivity to TDS but has low sensitivity to the feedback intensity, thus the system has both high security and high robustness. This system has an uncomplicated synchronization structure and high flexibility, and it opens up a new direction for high-quality chaos generation.