Abstract
The ability to identify characteristics of dynamical systems is important because it allows the understanding and modeling of complex physical phenomena that can be utilized in many applications, ranging from finance, environment, health, to industry. Here the concepts of convolution and pooling in deep learning are introduced to compute the eigenvalues of fuzzy recurrences obtained from the phase-space reconstructions of time series. Computer results show that the largest eigenvalues of convolved fuzzy recurrence plots can differentiate between cohorts of random and chaotic signals.
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.
