I have a question regarding how to calculate synchronization in two coupled rossler systems?
say x1 and x2 represent two variables of coupled system.The time series contains 5000 data points each.
is the formalue cpr=phasesynchro(x1,x2,m,t,e) correct for such calculations. however if i change the coupling parameters then also the value of cpr comes close to one[/.Do
we need to specify a window if so how?Also how to plot the p(tau) vs tau individually?
P.SI am novice in this field pardon me if the question is irrelevant.
correlation using probability of recurrence

 Junior
 Posts: 1
 Joined: Sat Jul 4, 2015 07:33
 Affiliation (Univ., Inst., Dept.): Jadavpur University
 Location: Jadavpur University Kolkata32
 Research field: nonlinear time series analysis
 Norbert
 Expert
 Posts: 183
 Joined: Wed Jan 4, 2006 11:03
 Affiliation (Univ., Inst., Dept.): Potsdam Institute for Climate Impact Research, Germany
 Location: Potsdam, Germany
 Location: Potsdam Institute for Climate Impact Research, Germany
Re: correlation using probability of recurrence
Hi,
your solution looks correct. Perhaps the used time window is too small. You can add another argument that specifies the time window (i.e. the maximal time range that will be considered by this method).
If you need the p(tau) separately, you can calculate them by the crqad function:
I have not tested this code but it should do something that corresponds to your needs. The difference might be that this function calculates the RQA measures from the standard RP, where the line of identity is the main diagonal. But the phasesynchro function calculates the measures from the variation of the RP, where the line of identity equals the xaxis.
Best regards
Norbert
your solution looks correct. Perhaps the used time window is too small. You can add another argument that specifies the time window (i.e. the maximal time range that will be considered by this method).
If you need the p(tau) separately, you can calculate them by the crqad function:
Code: Select all
y = crqad(timeseries,m,tau,epsilon,max_timerange);
plot(max_timerange:max_timerange,y.RRp)
Best regards
Norbert