Abstract
This paper presents a differential entropy optimization method for phase space reconstruction parameters. The degree of differential entropy can be used to describe the complexity of the system. By establishing the relationship between the differential entropy and the embedded dimension and the delay time, the objective function is obtained. The constraints of embedding dimension and delay time is established by describing the global features of the system through autocorrelation function. By using the improved particle swarm algorithm to solve the model, the minimum differential entropy is obtained and the best embedded dimension and delay time is obtained. Therefore, the reconstructed phase space not only maintains its independence but also maintains its dynamic characteristics. Finally, through the prediction of the annual data of sunspots and the simulation data from Lorenz system and, it is verified that the method can determine the appropriate embedding dimension and delay time.
Sign-in/Register to access full text options 
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.
