混沌工具箱

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