Noise Compression for Chaotic Sequences

This article explores a noise compression program designed specifically for chaotic sequences. The program utilizes Principal Component Analysis (PCA) as its core algorithm, which works by identifying the most significant patterns in the data and reconstructing the signal while filtering out noise components. Through eigenvalue decomposition of the covariance matrix, PCA effectively separates signal from noise subspace. The implementation demonstrates remarkable effectiveness, capable of completely eliminating 100% additive noise from simulated signals. Notably, the program features a user-friendly interface that allows non-specialists to operate it easily. The algorithm incorporates adaptive thresholding mechanisms that automatically adjust to different noise conditions, making it suitable for various application scenarios. Key functions include data preprocessing, covariance matrix computation, eigenvalue sorting, and signal reconstruction through inverse transformation. In summary, this noise compression program serves as a powerful and practical tool that significantly enhances performance in signal processing applications.