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author | Suraj Yerramilli | 2016-02-27 14:04:26 +0530 |
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committer | Suraj Yerramilli | 2016-02-27 14:04:26 +0530 |
commit | 9a33548afc4fac3275bdbcffc87f8ba3a57c17fb (patch) | |
tree | 80753face97359e56aeeac8dca486f2ffb286bb9 /man | |
parent | d04167e1355a9981dac447d82e44c0bb6f2f4e1e (diff) | |
download | SysID-R-code-9a33548afc4fac3275bdbcffc87f8ba3a57c17fb.tar.gz SysID-R-code-9a33548afc4fac3275bdbcffc87f8ba3a57c17fb.tar.bz2 SysID-R-code-9a33548afc4fac3275bdbcffc87f8ba3a57c17fb.zip |
added option to specify noise variance
Diffstat (limited to 'man')
-rw-r--r-- | man/idpoly.Rd | 16 |
1 files changed, 11 insertions, 5 deletions
diff --git a/man/idpoly.Rd b/man/idpoly.Rd index 3f82594..7c33be9 100644 --- a/man/idpoly.Rd +++ b/man/idpoly.Rd @@ -4,20 +4,25 @@ \alias{idpoly} \title{Polynomial model with identifiable parameters} \usage{ -idpoly(A = 1, B = 1, C = 1, D = 1, F1 = 1, ioDelay = 0, Ts = 1) +idpoly(A = 1, B = 1, C = 1, D = 1, F1 = 1, ioDelay = 0, Ts = 1, + noiseVar = 1, unit = c("seconds", "minutes", "hours", "days")[1]) } \arguments{ -\item{A}{Autoregressive coefficients} +\item{A}{autoregressive coefficients} -\item{B, F1}{Coefficients of the numerator and denominator respectively +\item{B, F1}{coefficients of the numerator and denominator respectively of the deterministic model between the input and output} -\item{C, D}{Coefficients of the numerator and denominator respectively +\item{C, D}{coefficients of the numerator and denominator respectively of the stochastic model} \item{ioDelay}{the delay in the input-output channel} \item{Ts}{sampling interval} + +\item{noiseVar}{variance of the white noise source (Default=\code{1})} + +\item{unit}{time unit (Default=\code{"seconds"})} } \description{ Creates a polynomial model with identifiable coefficients @@ -31,7 +36,8 @@ Discrete-time polynomials are of the form } \examples{ # define output-error model -mod_oe <- idpoly(B=c(0.6,-0.2),F1=c(1,-0.5),ioDelay = 2,Ts=0.1) +mod_oe <- idpoly(B=c(0.6,-0.2),F1=c(1,-0.5),ioDelay = 2,Ts=0.1, +noiseVar = 0.1) # define box-jenkins model B <- c(0.6,-0.2) |