A Comprehensive Collection of Stochastic Resonance Examples
A comprehensive collection of multiple stochastic resonance examples with MATLAB implementation references and algorithm demonstrations for signal processing applications.
Stochastic Resonance using Runge-Kutta Algorithm
MATLAB source code for simulating stochastic resonance phenomena through Runge-Kutta numerical integration method
Runge-Kutta Solution Method for Stochastic Resonance Systems
The Runge-Kutta method for stochastic resonance provides response signals for arbitrary stochastic resonance systems, enabling comprehensive system analysis through numerical simulation of dynamic behaviors.
Calculation of Input-Output Signal-to-Noise Ratio Gain for Bistable Stochastic Resonance Systems
gongzhenQ.m: Computes the input-output SNR (Signal-to-Noise Ratio) gain for bistable stochastic resonance systems, demonstrating pronounced stochastic resonance phenomena through quantitative analysis and parameter modulation.
Stochastic Resonance Methods and Applications for Weak Signal Detection
Doctoral Dissertation on Stochastic Resonance Techniques for Weak Signal Detection featuring Algorithm Analysis and Implementation Approaches
Simulation of Weak Signal Detection via Stochastic Resonance
Simulation of weak signal detection using stochastic resonance principles, including source code implementation of the fourth-order Runge-Kutta algorithm for solving the Langevin equation.
Genetic Algorithm Optimized Stochastic Resonance
Application Context: Stochastic Resonance (SR) utilizes noise to enhance signal detection. In bistable systems, parameters a and b in the Langevin equation critically impact system performance and require careful selection. The package includes two GA (Genetic Algorithm) implementation examples - one simplified and one advanced - demonstrating parameter optimization. Technical Innovation: Unlike conventional noise suppression methods, SR leverages environmental noise for signal amplification. The genetic algorithm systematically optimizes system parameters through fitness-based selection, crossover, and mutation operations.
MATLAB Implementation for Stochastic Resonance Similarity Computation
A MATLAB program for calculating stochastic resonance similarity, demonstrating how input-output similarity S improves with increasing noise standard deviation until reaching a saturation threshold. The implementation includes noise injection mechanisms and similarity quantification algorithms.
Stochastic Resonance
A basic stochastic resonance implementation with functional code demonstration