MATLAB Implementation for Stochastic Resonance Similarity Computation

This MATLAB computational program calculates stochastic resonance similarity metrics. The core algorithm dynamically adjusts noise levels while measuring the correlation between input and output signals. As the standard deviation of the injected noise increases, the input-output similarity index S shows progressive enhancement until converging to a saturation value. Researchers can utilize this code to analyze noise-induced optimization effects in signal processing systems. The program structure includes three key modules: 1) Gaussian noise generator with adjustable standard deviation parameters, 2) Signal similarity calculator using cross-correlation or mutual information methods, and 3) Iterative optimization loop for scanning noise-intensity relationships. Users should have MATLAB installed with basic programming knowledge to modify parameters like noise ranges and similarity thresholds. For optimal results, consider adjusting the step size for noise variation and selecting appropriate similarity metrics based on your signal characteristics. Further optimization techniques, such as adaptive noise injection strategies, can be implemented by referencing scholarly literature on stochastic resonance theory. This tool provides a practical framework for studying noise-enhanced signal processing phenomena in scientific research applications.