A Parameter-Selection-Based Chaotic System

Control parameters play a key role in a chaotic system, where they directly determine its chaotic performance. In this brief, we propose a novel parameter-selection-based chaotic system (PSCS), where a new sequence of control parameters is generated using Kolmogorov entropy (KE). KE measures the chaotic behaviors of a chaotic system, and the positive values indicate that the system exhibits chaotic characteristics. Hence, the new sequence is formed by the parameters that yield positive KE values, which always maintain the chaotic behaviors in each iteration. Coupling the obtained sequence into a basic chaotic map, PSCS achieves a simple structure and lower computation cost. Experiments on the time complexity, NIST SP800-22 test suit, and information entropy demonstrate the good performance of the proposed PSCS.