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Description: 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
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Size: 8192 |
Author: shupeng |
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Description: A time series Lyapunov exponent estimation for small data set
Platform: |
Size: 4096 |
Author: Yiyao |
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Description: 计算给定时间序列的最大Lyapunov指数-compute the maximal Lyapunov exponent of a time series
Platform: |
Size: 4096 |
Author: WangZhiliang |
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Description: 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.
Platform: |
Size: 4096 |
Author: Hesham |
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Description: Lyapunov exponent for time seires.
Platform: |
Size: 4096 |
Author: Yuanlong |
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Description: 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
Platform: |
Size: 4096 |
Author: wangke lin |
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