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[Other] Rosenstein_lyapunov_c DL : 0: Rosenstein方法(小数据量法)计算 Lyapunov 指数的 Matlab 程序-Rosenstein methods (small data volume method) calculation of Lyapunov index of Matlab procedures
Date : 2025-07-16 Size : 8kb User : shupeng
[matlab] lyaprosen DL : 0: A time series Lyapunov exponent estimation for small data set
Date : 2025-07-16 Size : 4kb User : Yiyao
[matlab] lyaprosen DL : 0: 计算给定时间序列的最大Lyapunov指数-compute the maximal Lyapunov exponent of a time series
Date : 2025-07-16 Size : 4kb User : WangZhiliang
[matlab] lyaprosen DL : 0: INPUTES: y: y is vector of values(time series data) tau: embedding lag of state space reconstruction. When you have not any information about tau please let it zero. The code will calculates the tau. m: m is embedding dimension. If you have not any information about embedding dimension please let it zero. the code will find proper embedding dimension. OUTPUTS: LLE: Largest Lyapunov Exponent lambda: Lyapunov exponents for various ks. Plot of this exponents is very helpful. If embedding dimension be selected correctly lambda curve will have smooth part(or fairly horizontal). If there is no smooth section on the curve, it is better you try with other embedding dimensions.- INPUTES: y: y is vector of values(time series data) tau: embedding lag of state space reconstruction. When you have not any information about tau please let it zero. The code will calculates the tau. m: m is embedding dimension. If you have not any information about embedding dimension please let it zero. the code will find proper embedding dimension. OUTPUTS: LLE: Largest Lyapunov Exponent lambda: Lyapunov exponents for various ks. Plot of this exponents is very helpful. If embedding dimension be selected correctly lambda curve will have smooth part(or fairly horizontal). If there is no smooth section on the curve, it is better you try with other embedding dimensions.
Date : 2025-07-16 Size : 4kb User : Hesham
[Documents] lyaprosen DL : 0: Lyapunov exponent for time seires.
Date : 2025-07-16 Size : 4kb User : Yuanlong
[matlab] lyaprosen DL : 0: NOTE1: When user do not have any information about tau, she should let tau equal to zero(0). In this case the code will use autocorrelation up to orders 10 to select proper embedding lag(tau). The proper lag is the lag before of first decline of autocorrelation value below exp(-1)=0.367879441.For data with nonlinear dependency autocorrelation function is not proper and mutual information criteria will be used for selecting proper lag value(tau). when both of criteria , Autocorrelation and mutual information fail to select tau, tau=1 is selected automatically- NOTE1: When user do not have any information about tau, she should let tau equal to zero(0). In this case the code will use autocorrelation up to orders 10 to select proper embedding lag(tau). The proper lag is the lag before of first decline of autocorrelation value below exp(-1)=0.367879441.For data with nonlinear dependency autocorrelation function is not proper and mutual information criteria will be used for selecting proper lag value(tau). when both of criteria , Autocorrelation and mutual information fail to select tau, tau=1 is selected automatically
Date : 2025-07-16 Size : 4kb User : wangke lin