Open AccessAdaptive identification for hyperchaotic l system based on Weiner model
Author(s) -
Yi Zhao,
Xiuzai Zhang,
Xinyu Sun
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.130503
Subject(s) - control theory (sociology) , piecewise linear function , chaotic , computer science , system identification , nonlinear system , adaptive filter , filter (signal processing) , convergence (economics) , kernel adaptive filter , identification (biology) , synchronization (alternating current) , mathematics , algorithm , filter design , artificial intelligence , physics , biology , measure (data warehouse) , botany , channel (broadcasting) , database , economic growth , computer network , geometry , control (management) , quantum mechanics , computer vision , economics
In order to be able to identify the hyper-chaotic l system with uncertain parameters effectively in real time, so that hyper-chaotic system control and synchronization tracking can be applied, this paper presents a system identification method for the hyper-chaotic system based on Wiener model. The linear part of the Wiener model consists of linear transversal filters, while the nonlinear part is represented approximately by piecewise linear filters. According to the minimum mean square error criterion, the filter parameter updated algorithm is derived, and the convergence condition is also obtained. Simulation results confirm the effectiveness of the adaptive filter for the identification of hyper-chaotic systems. The presented method not only overcomes the difficulty to identify a strongly nonlinear system only by adaptive linear filters, but also have a lower computational complexity compared with other non-linear adaptive filters.