Fractal Classification Method for Complex Signals

The classification of complex signals, often characterized by high noise levels, non-linearity, and overlapping patterns, poses significant challenges in signal processing. Traditional methods frequently fail to address the intricate structures inherent in such signals, necessitating the adoption of advanced analytical techniques. This study explores the application of fractal methodologies, leveraging their self-similar and scale-invariant properties, for classifying complex signals. By employing tools such as the Hurst exponent and the (R/R) method, this work demonstrates how fractal analysis can effectively characterize and categorize stationary and non-stationary signals based on their probabilistic distributions and fractal dimensions. Results indicate that fractal methods provide robust descriptors for distinguishing signal types, enabling enhanced accuracy and operational efficiency. The proposed approach holds promise for developing virtual analyzers with expansive dynamic ranges, applicable in diverse fields such as diagnostics, control systems, and signal processing.