Abstract
Predictability of chaotic systems is limited, in addition to the precision of the knowledge of the initial conditions, by the error of the models used to extract the nonlinear dynamics from the time series. In this paper, we analyze the predictions obtained from the anticipated synchronization scheme using a chain of slave neural network approximate replicas of the master system. We compare the maximum prediction horizons obtained with those attainable using standard prediction techniques.
Highlights
Modeling and predicting the dynamics of nonlinear chaotic systems is a challenging problem with important realworld applicationsstock market returns1͔, weather forecast2͔, etc.͒
We have considered two alternative practical techniques to anticipate the dynamics of chaotic systems
A neural network trained to the available data which can be iterated forward in time reproducing the same orbit of the chaotic system up to a given horizon
Summary
Modeling and predicting the dynamics of nonlinear chaotic systems is a challenging problem with important realworld applicationsstock market returns1͔, weather forecast2͔, etc.͒. In practice, the original system is unknown and approximate models fitted to the available data are used to model and forecast its nonlinear dynamicse.g., neural networks3͔͒. We analyze this problem using neural networks, one of the most popular nonparametric statistical learning techniques, for approximating the nonlinear dynamics from the available datatime series ͓7͔.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
