Improving the pseudo-randomness properties of chaotic maps using deep-zoom

TL;DR

This paper introduces a 'deep-zoom' method to enhance the pseudo-randomness of chaotic maps, demonstrating improved randomization and proposing a PRNG that passes standard randomness tests, comparable to traditional generators.

Contribution

The paper presents a novel deep-zoom approach to improve chaotic map randomness and develops a PRNG based on the k-logistic map that passes standard tests.

Findings

Rapid randomization of chaotic patterns observed.

Proposed PRNG passes DIEHARD and NIST tests.

Logistic map can serve as an effective PRNG.

Abstract

A generalized method is proposed to compose new orbits from a given chaotic map. The method provides an approach to examine discrete-time chaotic maps in a "deep-zoom" manner by using $k$-digits to the right from the decimal separator of a given point from the underlying chaotic map. Interesting phenomena have been identified. Rapid randomization was observed, i.e. chaotic patterns tend to become indistinguishable, when compared to the original orbits of the underlying chaotic map. Our results were presented using different graphical analyses (i.e., time-evolution, bifurcation diagram, Lyapunov exponent, Poincar\'e diagram and frequency distribution). Moreover, taking advantage of this randomization improvement, we propose a Pseudo-Random Number Generator (PRNG) based on the $k$-logistic map. The pseudo-random qualities of the proposed PRNG passed both tests successfully, i.e. DIEHARD and NIST, and were comparable with other traditional PRNGs such as the Mersenne Twister. The results suggest that simple maps such as the logistic map can be considered as good PRNG methods.