Explore MATLAB source code curated for "李雅普诺夫指数" with clean implementations, documentation, and examples.
Calculation of the Largest Lyapunov Exponent using Wolf's Method. This function computes the maximum Lyapunov exponent for time series data. Key parameters include: % m: embedding dimension % tau: time delay % data: input time series % N: length of time series % P: average period of the time series, which determines the search range for evolution phase points. When the current phase point is I, evolution phase points must satisfy |I-J|>P.
A MATLAB program for computing Lyapunov exponents with built-in usage instructions. Helpful for dynamical systems analysis, available for free download without any requirements.
MATLAB implementation for calculating Lyapunov exponents, which represent the numerical characteristics of average exponential divergence rates between adjacent trajectories in phase space. Also known as Lyapunov characteristic exponents, they serve as key indicators for identifying chaotic behavior in dynamical systems through numerical analysis.
Determining whether a sequence exhibits chaotic properties by computing the Lyapunov exponent of the logistic map, with implementation insights for numerical analysis
Implementation of Kolmogorov entropy calculation and Lyapunov exponent algorithms for determining whether a sequence exhibits chaotic behavior
Graduation project focusing on time series chaos analysis, implementing phase space reconstruction, Lyapunov exponent calculation, wavelet denoising techniques with Python/MATLAB code examples
Chaos Time Series Prediction Toolbox featuring Lyapunov exponent calculation, fractal dimension analysis, embedding dimension estimation, and neural network-based forecasting implementations
Implementing the Lorenz model and calculating Lyapunov exponents using MATLAB with code-based explanations
Source codes for nonlinear systems learning including Lyapunov exponent calculation, Lorenz attractor visualization, boost switching converters, bifurcation diagrams and more, suitable for nonlinear system simulation and study
Visualizing Lyapunov exponent curves for chaotic systems with implementation insights