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Diffstat (limited to 'arx.sci')
| -rw-r--r-- | arx.sci | 110 |
1 files changed, 110 insertions, 0 deletions
@@ -0,0 +1,110 @@ + +// Estimates Discrete time ARX model +// A(q)y(t) = B(q)u(t) + e(t) +// Current version uses random initial guess +// + +// Authors: Ashutosh,Harpreet,Inderpreet +// Updated(12-6-16) +function sys = arx(varargin) + [lhs , rhs] = argn(); + if ( rhs < 2 ) then + errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be 2"), "arx", rhs); + error(errmsg) + end + + z = varargin(1) + if typeof(z) == 'iddata' then + Ts = z.Ts;unit = z.TimeUnit + z = [z.OutputData z.InputData] + elseif typeof(z) == 'constant' then + Ts = 1;unit = 'seconds' + end + if ((~size(z,2)==2) & (~size(z,1)==2)) then + errmsg = msprintf(gettext("%s: input and output data matrix should be of size (number of data)*2"), "arx"); + error(errmsg); + end + + if (~isreal(z)) then + errmsg = msprintf(gettext("%s: input and output data matrix should be a real matrix"), "arx"); + error(errmsg); + end + + n = varargin(2) + if (size(n,"*")<2| size(n,"*")>3) then + errmsg = msprintf(gettext("%s: The order and delay matrix [na nb nk] should be of size [2 3]"), "arx"); + error(errmsg); + end + + if (size(find(n<0),"*") | size(find(((n-floor(n))<%eps)== %f))) then + errmsg = msprintf(gettext("%s: values of order and delay matrix [na nb nk] should be nonnegative integer number "), "arx"); + error(errmsg); + end + + na = n(1); nb = n(2); //nk = n(3); //nf = n(4); +// + if (size(n,"*") == 2) then + nk = 1 + else + nk = n(3); + end + + // storing U(k) , y(k) and n data in UDATA,YDATA and NDATA respectively + YDATA = z(:,1); + UDATA = z(:,2); + NDATA = size(UDATA,"*"); + function e = G(p,m) + e = YDATA - _objfunarx(UDATA,YDATA,p,na,nb,nk); + endfunction + tempSum = na+nb + p0 = linspace(0.1,0.9,tempSum)'; + [var,errl] = lsqrsolve(p0,G,size(UDATA,"*")); + err = (norm(errl)^2); + opt_err = err; + resid = G(var,[]); + + a = 1-poly([var(nb+1:nb+na)]',"q","coeff"); + b = poly([repmat(0,nk,1);var(1:nb)]',"q","coeff"); + a = (poly([1,-coeff(a)],'q','coeff')) + t = idpoly(coeff(a),coeff(b),1,1,1,Ts) + + // estimating the other parameters + [temp1,temp2,temp3] = predict(z,t) + [temp11,temp22,temp33] = pe(z,t) + + estData = calModelPara(temp1,temp1,n(1)+n(2)) + //pause + t.Report.Fit.MSE = estData.MSE + t.Report.Fit.FPE = estData.FPE + t.Report.Fit.FitPer = estData.FitPer + t.Report.Fit.AIC = estData.AIC + t.Report.Fit.AICc = estData.AICc + t.Report.Fit.nAIC = estData.nAIC + t.Report.Fit.BIC = estData.BIC + t.TimeUnit = unit + sys = t + +endfunction + +function yhat = _objfunarx(UDATA,YDATA,x,na,nb,nk) + x=x(:) + q = poly(0,'q') + tempSum = nb+na + // 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) + maxDelay = max([length(aSize) length(bSize)]) + yhat = [YDATA(1:maxDelay)] + for k=maxDelay+1:size(UDATA,"*") + tempB = 0 + for ii = 1:size(bSize,'*') + tempB = tempB + bSize(ii)*UDATA(k-ii+1) + end + tempA = 0 + for ii = 1:size(aSize,"*") + tempA = tempA + aSize(ii)*YDATA(k-ii) + end + yhat = [yhat; [ tempA+tempB ]]; + end +endfunction |