混沌工具箱分析混沌的工具箱，十分的实用。

混沌工具箱分析混沌的工具箱，十分的实用

工具箱说明

1、该工具箱包括了混沌时间序列分析与预测的常用方法,有:

(1)产生混沌时间序列(chaotic time series)

Logistic映射 - \ChaosAttractors\Main_Logistic.m

Henon映射 - \ChaosAttractors\Main_Henon.m

Lorenz吸引子 - \ChaosAttractors\Main_Lorenz.m

Duffing吸引子 - \ChaosAttractors\Main_Duffing.m

Duffing2吸引子 - \ChaosAttractors\Main_Duffing2.m

Rossler吸引子 - \ChaosAttractors\Main_Rossler.m

Chens吸引子 - \ChaosAttractors\Main_Chens.m

Ikeda吸引子 - \ChaosAttractors\Main_Ikeda.m

MackeyGLass序列 - \ChaosAttractors\Main_MackeyGLass.m

Quadratic序列 - \ChaosAttractors\Main_Quadratic.m

(2)求时延(delay time)

自相关法 - \DelayTime_Others\Main_AutoCorrelation.m

平均位移法 - \DelayTime_Others\Main_AverageDisplacement.m

(去偏)复自相关法 - \DelayTime_Others\Main_ComplexAutoCorrelation.m

互信息法 - \DelayTime_MutualInformation\Main_Mutual_Information.m

(3)求嵌入维(embedding dimension)

假近邻法 - \EmbeddingDimension_FNN\Main_FNN.m

Cao方法 - \EmbeddingDimension_Cao\Main_EmbeddingDimension_Cao.m

(4)同时求时延与嵌入窗(delay time & embedding window)

CC方法 - \C-C Method\Main_CC_Luzhenbo.m

(5)求关联维(correlation dimension)

GP算法 - \CorrelationDimension_GP\Main_CorrelationDimension_GP.m

(6)求K熵(Kolmogorov Entropy)

GP算法 - \KolmogorovEntropy_GP\Main_KolmogorovEntropy_GP.m

STB算法 - \KolmogorovEntropy_STB\Main_KolmogorovEntropy_STB.m

(7)求最大Lyapunov指数(largest Lyapunov exponent)

小数据量法 - \LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein1.m

\LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein2.m

\LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein3.m

\LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein4.m

(8)求Lyapunov指数谱(Lyapunov exponent spectrum)

BBA算法 - \LyapunovSpectrum_BBA\Main_LyapunovSpectrum_BBA1.m

\LyapunovSpectrum_BBA\Main_LyapunovSpectrum_BBA2.m

(9)求二进制图形的盒子维(box dimension)和广义维(genealized dimension)

覆盖法 - \BoxDimension_2D\Main_BoxDimension_2D.m

- \GeneralizedDimension_2D\Main_GeneralizedDimension_2D.m

(10)求时间序列的盒子维(box dimension)和广义维(genealized dimension)

覆盖法 - \BoxDimension_TS\Main_BoxDimension_TS.m

- \GeneralizedDimension_TS\Main_GeneralizedDimension_TS.m

(11)混沌时间序列预测(chaotic time series prediction)

RBF神经网络一步预测 - \Prediction_RBF\Main_RBF.m

RBF神经网络多步预测 - \Prediction_RBF\Main_RBF_MultiStepPred.m

Volterra级数一步预测 - \Prediction_Volterra\Main_Volterra.m

Volterra级数多步预测 - \Prediction_Volterra\Main_Volterra_MultiStepPred.m

(12)产生替代数据(Surrogate Data)

随机相位法 - \SurrogateData\Main_SurrogateData.m