@inproceedings{ff19e59cbca5450c988355ef6d255bda,
title = "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.",
keywords = "Chaos, neural filter, non-Gaussian noise, nonlinear Kalman filter, synchronization",
author = "Yu Guo and Fei Wang and Lo, \{James Ting Ho\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7953041",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4666--4670",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}