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. 2019 Mar 30;46(2):193–210. doi: 10.1007/s10928-019-09629-4

Table 4.

Estimation of parameters that enter in a linear fashion by chaos synchronization (CS), nonlinear least squares (NLS) and extended least squares (ELS)

Estimation method Nominal CS NLS ELS
Noiseless system with dense dataa
 k1 0.0666 0.069 0.01 0.0666
 k2 0.0333 0.0344 0.01 0.0333
Noisy systemb with dense dataa
 k1 0.0666 0.0629 0.01 0.0666
 k2 0.0333 0.0340 0.01 0.0333
Noiseless system with sparse datac
 k1 0.0666 0.0615 0.01 0.0666
 k2 0.0333 0.0303 0.01 0.0333

To estimate parameters for the noisy system we filter the data using the wden function provided by MATLAB (MathWorks: MA) version R2017a as input to the CS, NLS or ELS method

aDense data signifies data sampled at 1 min intervals

bNoisy system signifies 20% proportional error

cSparse data signifies data sampled at 45 min intervals