混沌工具箱剖析杂乱的工具箱，十分的好用

【工具箱表明】

1、该工具箱包含了杂乱时间序列剖析与预测的常见方式,有:

(1)造成杂乱时间序列(chaotic time series)

Logistic投射 - \ChaosAttractors\Main_Logistic.m

Henon投射 - \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编码序列 - \ChaosAttractors\Main_MackeyGLass.m

Quadratic编码序列 - \ChaosAttractors\Main_Quadratic.m

(2)求时延(delay time)

自相关法 - \DelayTime_Others\Main_AutoCorrelation.m

均值位移法 - \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)求置入维(embedding dimension)

假邻近法 - \EmbeddingDimension_FNN\Main_FNN.m

Cao方式 - \EmbeddingDimension_Cao\Main_EmbeddingDimension_Cao.m

(4)另外求时延与置入窗(delay time amp; embedding window)

CC方式 - \C-C Method\Main_CC_Luzhenbo.m

(5)求关系维(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)求较大 Lyapunov指数值(largest Lyapunov exponent)

小信息量法 - \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指数值谱(Lyapunov exponent spectrum)

BBA算法 - \LyapunovSpectrum_BBA\Main_LyapunovSpectrum_BBA1.m

\LyapunovSpectrum_BBA\Main_LyapunovSpectrum_BBA2.m

(9)求二进制图型的小盒子维(box dimension)和理论维(genealized dimension)

遮盖法 - \BoxDimension_3D\Main_BoxDimension_3D.m

- \GeneralizedDimension_3D\Main_GeneralizedDimension_3D.m

(10)求时间序列的小盒子维(box dimension)和理论维(genealized dimension)

遮盖法 - \BoxDimension_TS\Main_BoxDimension_TS.m

- \GeneralizedDimension_TS\Main_GeneralizedDimension_TS.m

(11)杂乱时间序列预测(chaotic time series prediction)

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

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

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

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

(12)造成取代数据信息(Surrogate Data)

任意相位差法 - \SurrogateData\Main_SurrogateData.m