Explore MATLAB source code curated for "混沌时间序列分析" with clean implementations, documentation, and examples.
Chaos Time Series Analysis and Prediction Toolbox (Version 2.9) developed by Dr. Zhenbo Lu from Naval Engineering University, featuring complete MATLAB source code implementations for chaotic system modeling and forecasting algorithms
This toolbox encompasses common approaches for chaotic time series analysis and prediction, featuring implementations for generating chaotic time series through various attractor models: - Logistic Map (ChaosAttractorsMain_Logistic.m) - Simulates population growth dynamics using iterative equations - Henon Map (ChaosAttractorsMain_Henon.m) - Models two-dimensional chaotic system with quadratic nonlinearity - Lorenz Attractor (ChaosAttractorsMain_Lorenz.m) - Solves three differential equations for atmospheric convection patterns - Duffing Attractor (ChaosAttractorsMain_Duffing.m) - Implements nonlinear oscillator with periodic forcing - Duffing2 Attractor (ChaosAttractorsMain_Duffing2.m) - Extended Duffing system variant
The G-P (Grassberger-Procaccia) method for chaotic time series analysis, used to calculate correlation dimension and embedding dimension required for phase space reconstruction with implementation insights
Chaotic Time Series Analysis and Prediction MATLAB Toolbox - Version 1.0 featuring comprehensive algorithms for phase space reconstruction, Lyapunov exponent calculation, and nonlinear prediction models
Version 2.9 of Lu Zhenbo's Chaotic Time Series Analysis and Prediction Toolbox - A comprehensive MATLAB-based toolkit with advanced algorithms for nonlinear dynamics analysis and forecasting
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.
Comprehensive Chaos Time Series Analysis and Prediction Toolbox featuring multiple analytical methodologies and forecasting algorithms with practical implementation support.
Chaos Time Series Analysis and Prediction Toolkit Version 2.0 - Advanced algorithms for nonlinear dynamics analysis, phase space reconstruction, Lyapunov exponent calculation, and multi-step prediction with ML/AI integration