A neural filter-based scheme for synchronizing chaotic systems

Abstract

Synchronization of chaotic systems and/or maps is a key step to implement secure communication schemes with chaos. If the process to synchronize chaotic systems is modeled stochastic, schemes based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) have been studied in the past. However, such nonlinear filters are employed with assumptions of Gaussian noise processes and the Markov property. Further, EKF and UKF are suboptimal filtering methods, incurring unacceptable errors for high nonlinear systems. In this paper, neural filter (NF) is proposed for chaotic synchronization. This new approach requires no mentioned assumptions and achieves optimal filter. Numerical comparisons between the proposed approach and existing schemes are presented in this paper, showing the superiority of the proposed approach.