Renormalized entropy for one dimensional discrete maps: periodic and quasi-periodic route to chaos and their robustness
O. Afsar, G. B. Bagci, U. Tirnakli

TL;DR
This paper demonstrates that renormalized entropy effectively detects bifurcations, accumulation points, and quasi-periodic regimes in one-dimensional maps, proving its robustness as a complexity measure across different dynamical behaviors.
Contribution
It introduces and validates the use of renormalized entropy as a reliable measure for identifying critical transitions and self-similar structures in logistic and sine-circle maps.
Findings
Renormalized entropy decreases before accumulation points in logistic map.
It detects self-similar windows via abrupt changes in entropy.
Oscillatory behavior of entropy indicates quasi-periodic regimes in sine-circle map.
Abstract
We apply renormalized entropy as a complexity measure to the logistic and sine-circle maps. In the case of logistic map, renormalized entropy decreases (increases) until the accumulation point (after the accumulation point up to the most chaotic state) as a sign of increasing (decreasing) degree of order in all the investigated periodic windows, namely, period-2, 3, and 5, thereby proving the robustness of this complexity measure. This observed change in the renormalized entropy is adequate, since the bifurcations are exhibited before the accumulation point, after which the band-merging, in opposition to the bifurcations, is exhibited. In addition to the precise detection of the accumulation points in all these windows, it is shown that the renormalized entropy can detect the self-similar windows in the chaotic regime by exhibiting abrupt changes in its values. Regarding the sine-circle…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
