Explore MATLAB source code curated for "C_C方法" with clean implementations, documentation, and examples.
Implementation of C-C method algorithms for computing optimal time delay and embedding dimension, with applications in Lyapunov exponent calculation, correlation dimension analysis, and chaotic time series prediction. Includes MATLAB/Python code insights for signal processing and nonlinear dynamics.
MATLAB implementation of C-C method and enhanced C-C method for phase space reconstruction - Processing performance: 3 minutes for 3000 data points - Key references: 1) "Nonlinear dynamics, delay times, and embedding windows.pdf" 2) "Phase space reconstruction parameter selection based on improved C-C method 4.pdf" - Directory structure includes C_C_Method_luzhenbo main file with specialized functions for chaotic system data generation and correlation integral calculations
Program Design for Computing Correlation Integral and Time Delay via C-C Method
The C-C method in chaotic time series analysis is used to determine time window and delay time for phase space reconstruction, involving algorithm implementation strategies and key parameter calculations.
MATLAB Chaos Toolbox featuring C_C method and Wolf algorithm for computing largest Lyapunov exponents. Unlike Zhenbo Lu's toolbox, this version provides fully visible functions with complete source code accessibility. All implementations are open for modification and completely free.