diff options
Diffstat (limited to 'ar.sci')
| -rw-r--r-- | ar.sci | 61 |
1 files changed, 48 insertions, 13 deletions
@@ -1,11 +1,46 @@ -// Estimates Discrete time AR model -// A(q)y(t) = e(t) -// Current version uses random initial guess -// Authors: Ashutosh,Harpreet,Inderpreet -// Updated(12-6-16) -function sys = ar(varargin) -// +function sys = ar(varargin) +// Parameters Estimation of AR model using Input Output time-domain data +// +// Calling Sequence +// sys = ar(ioData,[na]) +// +// Parameters +// ioData : iddata or [outputData inputData] ,matrix of nx2 dimensions, type plant data +// na : non-negative integer number specified as order of the polynomial A(z^-1) +// sys : idpoly type polynomial have estimated coefficients of A(z^-1) polynomials +// +// Description +// Fit AR model on given input output data +// The mathematical equation of the AR model +// <latex> +// begin{eqnarray} +// A(q)y(t) = e(t) +// end{eqnarray} +// </latex> +// It is SISO type model. It minimizes the sum of the squares of nonlinear functions using Levenberg-Marquardt algorithm. +// sys ,an idpoly type class, have different fields that contains estimated coefficients, sampling time, time unit and other estimated data in Report object. +// +// Examples +// u = idinput(1024,'PRBS',[0 1/20],[-1 1]) +// a = [1 0.5];b = [0 2 3]; +// model = idpoly(a,b,'Ts',0.1); +// y = sim(u,model) + rand(length(u),1); +// plantData = iddata(y,[],0.1); +// sys = ar(plantData,[2]) +// +// Examples +// u = idinput(1024,'PRBS',[0 1/20],[-1 1]); +// a = [1 0.5];b = [0 0.2 0.3]; +// model = idpoly(a,b,'Ts',0.1); +// y = sim(u,model) + rand(length(u),1); +// plantData = [y]; +// sys = ar(plantData,[2]) +// +// Authors +// Ashutosh Kumar Bhargava, Bhushan Manjarekar + + [lhs , rhs] = argn(); if ( rhs < 2 ) then errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be 2"), "ar", rhs); @@ -41,7 +76,7 @@ function sys = ar(varargin) end na = n; nb = 0; nk = 0; - // storing U(k) , y(k) and n data in UDATA,YDATA and NDATA respectively + // storing U(k) , y(k) and n data in UDATA,YDATA and NDATA respectively YDATA = z(:,1); UDATA = zeros(size(z,1),1) NDATA = size(UDATA,"*"); @@ -59,12 +94,12 @@ function sys = ar(varargin) a = (poly([1,-coeff(a)],'q','coeff')) t = idpoly(coeff(a),1,1,1,1,Ts) - // estimating the other parameters + // estimating the other parameters [temp1,temp2,temp3] = predict([YDATA UDATA],t) [temp11,temp22,temp33] = pe([YDATA UDATA],t) estData = calModelPara(temp1,temp1,n(1)) - //pause + // pause t.Report.Fit.MSE = estData.MSE t.Report.Fit.FPE = estData.FPE t.Report.Fit.FitPer = estData.FitPer @@ -74,15 +109,15 @@ function sys = ar(varargin) t.Report.Fit.BIC = estData.BIC t.TimeUnit = unit sys = t - //sys = idpoly(coeff(a),1,1,1,1,Ts) -// sys.TimeUnit = unit + // sys = idpoly(coeff(a),1,1,1,1,Ts) +// sys.TimeUnit = unit endfunction function yhat = _objfun(UDATA,YDATA,x,na,nb,nk) x=x(:) q = poly(0,'q') tempSum = nb+na - // making polynomials + // making polynomials b = poly([repmat(0,nk,1);x(1:nb)]',"q","coeff"); a = 1 - poly([x(nb+1:nb+na)]',"q","coeff") aSize = coeff(a);bSize = coeff(b) |