A high efficient next generation reservoir computing to predict and generate chaos with application for secure communication

In this work, a high efficient next generation reservoir computing (HENG-RC) paradigm that adopts the principle of local states correlation and attention mechanism is proposed, which is able to process dynamical information generated by both the low dimensional and very large spatiotemporal chaotic systems (VLSCS). From a dynamical system perspective, the dynamical characteristics such as density distribution, Poincaré plots and max Lyapunov exponents of the proposed HENG-RC are studied. It is revealed that the trained model can be seen as a data-driven chaotic system. Furthermore, a novel scheme of secure communication based on chaotic synchronization of two HENG-RC systems is designed, of which the security is enhanced as the intruder needs to know simultaneously the training signal and details of the parameter setting in the HENG-RC. The digital implementation using field programmable gate array is experimentally realised, proving the feasibility of the secure communication scheme