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Diffstat (limited to 'man/oe.Rd')
| -rw-r--r-- | man/oe.Rd | 35 |
1 files changed, 18 insertions, 17 deletions
@@ -1,4 +1,4 @@ -% Generated by roxygen2 (4.1.1): do not edit by hand +% Generated by roxygen2: do not edit by hand % Please edit documentation in R/estpoly.R \name{oe} \alias{oe} @@ -9,18 +9,18 @@ oe(x, order = c(1, 1, 0), options = optimOptions()) \arguments{ \item{x}{an object of class \code{idframe}} -\item{options}{Estimation Options, setup using -\code{\link{optimOptions}}} - -\item{order:}{Specification of the orders: the four integer components +\item{order}{Specification of the orders: the four integer components (nb,nf,nk) are order of polynomial B + 1, order of the polynomial F, and the input-output delay respectively} + +\item{options}{Estimation Options, setup using +\code{\link{optimOptions}}} } \value{ An object of class \code{estpoly} containing the following elements: \tabular{ll}{ - \code{sys} \tab an \code{idpoly} object containing the + \code{sys} \tab an \code{idpoly} object containing the fitted OE coefficients \cr \code{fitted.values} \tab the predicted response \cr \code{residuals} \tab the residuals \cr @@ -31,43 +31,44 @@ An object of class \code{estpoly} containing the following elements: \code{vcov} \tab the covariance matrix of the fitted coefficients\cr \code{sigma} \tab the standard deviation of the innovations } \cr - \code{options} \tab Option set used for estimation. If no + \code{options} \tab Option set used for estimation. If no custom options were configured, this is a set of default options. \cr \code{termination} \tab Termination conditions for the iterative search used for prediction error minimization. \tabular{ll}{ \code{WhyStop} \tab Reason for termination \cr \code{iter} \tab Number of Iterations \cr - \code{iter} \tab Number of Function Evaluations - } + \code{iter} \tab Number of Function Evaluations + } } } \description{ Fit an output-error model of the specified order given the input-output data } \details{ -SISO OE models are of the form +SISO OE models are of the form \deqn{ - y[k] + f_1 y[k-1] + \ldots + f_{nf} y[k-nf] = b_{nk} u[k-nk] + + y[k] + f_1 y[k-1] + \ldots + f_{nf} y[k-nf] = b_{nk} u[k-nk] + \ldots + b_{nk+nb} u[k-nk-nb] + f_{1} e[k-1] + \ldots f_{nf} e[k-nf] - + e[k] + + e[k] } -The function estimates the coefficients using non-linear least squares +The function estimates the coefficients using non-linear least squares (Levenberg-Marquardt Algorithm) \\ -The data is expected to have no offsets or trends. They can be removed +The data is expected to have no offsets or trends. They can be removed using the \code{\link{detrend}} function. } \examples{ data(oesim) z <- dataSlice(data,end=1533) # training set mod_oe <- oe(z,c(2,1,2),optimOptions(tol=1e-04,LMinit=0.01)) -mod_oe +mod_oe plot(mod_oe) # plot the predicted and actual responses + } \references{ -Arun K. Tangirala (2015), \emph{Principles of System Identification: -Theory and Practice}, CRC Press, Boca Raton. Sections 14.4.1, 17.5.2, +Arun K. Tangirala (2015), \emph{Principles of System Identification: +Theory and Practice}, CRC Press, Boca Raton. Sections 14.4.1, 17.5.2, 21.6.3 } |