Research on the Strategy for the Flexible Configuration of Chaotic Signal Probability Distribution and Its Application

Given the constraints on the invariant distribution in chaotic systems, flexibly setting the probability distribution of chaotic signals poses a significant challenge. To tackle this issue, this paper proposes a strategy that transforms the task into solving and modifying the probability density function of the chaotic intrinsic signal. Initially, kernel density estimation algorithms are employed to address the issue of obtaining smooth probability density functions for high-dimensional chaotic signals. Any chaotic signal can serve as the intrinsic signal source, with its probability density function and distribution function being solvable using this algorithm. Subsequently, a graph-based transformation algorithm is introduced for the flexible adjustment of chaotic signal probability distribution. This algorithm can convert the intrinsic signal into a chaotic signal with the desired distribution type based on the characteristics of the target distribution, providing an analytical expression for the transformation relationship. Finally, the effectiveness of this strategy is validated by generating uniform distribution chaotic signals using a Chua chaotic signal as the intrinsic source. The outstanding performance of this signal in suppressing common-mode conducted electromagnetic interference in high-frequency converters is highlighted. The experimental results demonstrate this strategy’s ability to flexibly configure probability distribution types of chaotic signals. Additionally, chaotic signals with a uniform distribution can achieve uniform power spectrum shaping, with a suppression effect on maximum common-mode conducted electromagnetic interference reaching 16.56 dB.