diff options
| -rw-r--r-- | R/nonparam.R | 18 | ||||
| -rw-r--r-- | man/spa.Rd | 29 |
2 files changed, 24 insertions, 23 deletions
diff --git a/R/nonparam.R b/R/nonparam.R index 540207a..d1e56a9 100644 --- a/R/nonparam.R +++ b/R/nonparam.R @@ -162,7 +162,8 @@ step <- function(model){ #' (Default: \code{seq(1,128)/128*pi/Ts}) #' #' @return -#' an \code{idfrd} object containing the estimated frequency response +#' an \code{idfrd} object containing the estimated frequency response +#' and the noise spectrum #' #' @references #' Arun K. Tangirala (2015), \emph{Principles of System Identification: @@ -181,8 +182,10 @@ spa <- function(x,winsize=NULL,freq=NULL){ if(is.null(winsize)) winsize <- min(N/10,30) if(is.null(freq)) freq <- (1:128)/128*pi/deltat(x) M <- winsize + Ryu <- mult_ccf(x$out,x$input,lag.max = M) Ruu <- mult_ccf(x$input,x$input,lag.max=M) + Ryy <- mult_ccf(x$out,x$out,lag.max = M) cov2spec <- function(omega,R,M){ seq1 <- exp(-1i*(-M:M)*omega) @@ -190,14 +193,19 @@ spa <- function(x,winsize=NULL,freq=NULL){ } G <- array(0,c(nout,nin,length(freq))) + spec <- array(0,c(nout,nout,length(freq))) for(i in 1:nout){ + phi_y <- sapply(freq,cov2spec,Ryy[i,i,],M) + temp <- phi_y for(j in 1:nin){ - num <- sapply(freq,cov2spec,Ryu[i,j,],M) - den <- sapply(freq,cov2spec,Ruu[,j,],M) - G[i,j,] <- num/den + phi_yu <- sapply(freq,cov2spec,Ryu[i,j,],M) + phi_u <- sapply(freq,cov2spec,Ruu[j,j,],M) + G[i,j,] <- phi_yu/phi_u + temp <- phi_y - phi_yu*Conj(phi_yu)/phi_u } + spec[i,i,] <- temp } - out <- idfrd(G,matrix(freq),deltat(x)) + out <- idfrd(G,matrix(freq),deltat(x),spec) return(out) } @@ -4,28 +4,24 @@ \alias{spa} \title{Estimate frequency response} \usage{ -spa(data, npad = 255) +spa(x, winsize = NULL, freq = NULL) } \arguments{ -\item{data}{an \code{idframe} object} +\item{x}{an \code{idframe} object} -\item{npad}{an integer representing the total length of each time series -to analyze after padding with zeros. This argument allows the user to -control the spectral resolution of the SDF estimates: the normalized -frequency interval is deltaf=1/npad. (Default: 255)} +\item{freq}{frequency points at which the response is evaluated +(Default: \code{seq(1,128)/128*pi/Ts})} + +\item{W}{lag size of the Hanning window (Default: \code{min +(length(x)/10,30)})} } \value{ -an \code{idfrd} object containing the estimated frequency response +an \code{idfrd} object containing the estimated frequency response +and the noise spectrum } \description{ -Estimates Frequency Response with fixed frequency resolution using -spectral analysis -} -\details{ -The function calls the \code{SDF} function in the \code{sapa} package to -compute the cross-spectral densities. The method used is \strong{Welch's -Overlapped Segment Averaging} with a normalized \strong{Hanning} window. -The overlap used is 50%. +Estimates frequency response and noise spectrum from data with +fixed resolution using spectral analysis } \examples{ data(frf) @@ -36,7 +32,4 @@ frf <- spa(data) Arun K. Tangirala (2015), \emph{Principles of System Identification: Theory and Practice}, CRC Press, Boca Raton. Sections 16.5 and 20.4 } -\seealso{ -\code{\link[sapa]{SDF}} -} |