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[Mathimatics-Numerical algorithms] 混沌时间序列预测

Description: 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 2、在matlab环境中首先运行install.m,将工具箱所在路径添加至matlab 3、各子目录下以Main_开头的文件即是主程序文件,直接按快捷键F5运行即可 4、工具箱中所有程序均在Matlab6.5和Matlab7.1环境中调试通过，不能保证在Matlab其它版本正确运行。 5、工具箱中部分功能为试用版，敬请谅解！ 6、 作者：陆振波，海军工程大学 欢迎同行来信交流与合作，更多文章与程序下载请访问我的个人主页
Platform: | Size: 579972 | Author: niuchao0511 | Hits:

[matlab] 同时计算关联维和Kolmogorov熵

Description: 关于非线性时间序列中同时计算关联维和Kolmogorov熵的程序,-on nonlinear time series simultaneously calculate the correlation dimension and Kolmogorov entropy procedures,
Platform: | Size: 31744 | Author: gxsh | Hits:

[matlab] K_entropy

Description: 关于混沌Kolmogorov熵的计算程序，在前人基础上自己编写的。已经用过很多混沌时间序列K熵的计算。-On chaotic Kolmogorov entropy calculation procedure, based on predecessors have written. Has been used a lot of chaotic time series K entropy calculations.
Platform: | Size: 3072 | Author: 张真 | Hits:

[matlab] Entropy

Description: 在Matlab环境下，关于近似熵的计算程序
Platform: | Size: 1024 | Author: Nick | Hits:

[Other] chaos

Description: ApEn(近似熵)方法是测度熵的一种替代算 法,简单可靠,值得深入研究. -.The correlation dimension obtained from this method is optimal and the stable estimation of the Kolmogorov entropy is also obtained.
Platform: | Size: 101376 | Author: k | Hits:

[matlab] matlab

Description: 判断序列是否为混沌序列的kolmogorov熵值和李雅普诺夫指数计算-Determine whether a sequence is the Kolmogorov entropy of chaotic sequence and calculation of Lyapunov exponent
Platform: | Size: 1024 | Author: 黄子轩 | Hits:

[Program doc] Chaotic-time-series-analysis

Description: 混沌时间序列Matlab源程序,包含时间序列的时间延迟计算，关联积分计算，相空间重构，时间序列分解，Heaviside函数的计算，延迟时间和时间窗口计算，混沌吸引子关联维计算，重构相空间进行K_L变换，混沌吸引子关联维计算，Hurst指数分析，关联维和Kolmogorov熵计算，FFT计算序列平均周期，最大lyapunov指数计算，利用互信息法求时间延迟，混沌和噪声识别的源程序。-Matlab chaotic time series source, time includes the time series of delay calculation, correlation integral calculation, phase space reconstruction, time series decomposition, calculated Heaviside function, the delay time and the time window calculated correlation dimension of chaotic attractors calculated reconstruction phase space K_L transform computing correlation dimension of chaotic attractors, Hurst exponent analysis, correlation dimension and Kolmogorov entropy calculation, FFT calculation sequence averaging period, maximum lyapunov index, mutual information method the time delay, chaos and noise source identification.
Platform: | Size: 26624 | Author: 马喆 | Hits:

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