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// Estimates Discrete time BJ model
// y(t) = [B(q)/F(q)]u(t) + [C(q)/D(q)]e(t)
// Current version uses random initial guess
// Need to get appropriate guess from OE and noise models
// Authors: Ashutosh,Harpreet,Inderpreet
// Updated(12-6-16)
//function [theta_bj,opt_err,resid] = bj(varargin)
function sys = bj(varargin)
[lhs , rhs] = argn();
if ( rhs < 2 ) then
errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be 2"), "bj", 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"), "bj");
error(errmsg);
end
if (~isreal(z)) then
errmsg = msprintf(gettext("%s: input and output data matrix should be a real matrix"), "bj");
error(errmsg);
end
n = varargin(2)
if (size(n,"*")<4| size(n,"*")>5) then
errmsg = msprintf(gettext("%s: The order and delay matrix [nb nc nd nf nk] should be of size [4 5]"), "bj");
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 [nb nc nd nf nk] should be nonnegative integer number "), "bj");
error(errmsg);
end
nb = n(1); nc = n(2); nd = n(3); nf = n(4);
if (size(n,"*") == 4) then
nk = 1
else
nk = n(5);
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 - _objfun(UDATA,p,nd,nc,nf,nb,nk);
endfunction
tempSum = nb+nc+nd+nf
p0 = linspace(0.5,0.9,tempSum)';
[var,errl] = lsqrsolve(p0,G,size(UDATA,"*"));
err = (norm(errl)^2);
opt_err = err;
resid = G(var,[]);
b = poly([repmat(0,nk,1);var(1:nb)]',"q","coeff");
c = poly([1; var(nb+1:nb+nc)]',"q","coeff");
d = poly([1; var(nb+nc+1:nb+nc+nd)]',"q","coeff");
f = poly([1; var(nb+nd+nc+1:nd+nc+nf+nb)]',"q","coeff");
t = idpoly(1,coeff(b),coeff(c),coeff(d),coeff(f),Ts)
// estimating the other parameters
[temp1,temp2,temp3] = predict(z,t)
[temp11,temp22,temp33] = pe(z,t)
estData = calModelPara(temp1,temp11,sum(n(1:4)))
//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 = _objfun(UDATA,x,nd,nc,nf,nb,nk)
x=x(:)
q = poly(0,'q')
tempSum = nb+nc+nd+nf
// making polynomials
b = poly([repmat(0,nk,1);x(1:nb)]',"q","coeff");
c = poly([1; x(nb+1:nb+nc)]',"q","coeff");
d = poly([1; x(nb+nc+1:nb+nc+nd)]',"q","coeff");
f = poly([1; x(nb+nd+nc+1:nd+nc+nf+nb)]',"q","coeff");
bd = coeff(b*d); cf = coeff(c*f); fc_d = coeff(f*(c-d));
if size(bd,"*") == 1 then
bd = repmat(0,nb+nd+1,1)
end
maxDelay = max([length(bd) length(cf) length(fc_d)])
yhat = [YDATA(1:maxDelay)]
for k=maxDelay+1:size(UDATA,"*")
bdadd = 0
for i = 1:size(bd,"*")
bdadd = bdadd + bd(i)*UDATA(k-i+1)
end
fc_dadd = 0
for i = 1:size(fc_d,"*")
fc_dadd = fc_dadd + fc_d(i)*YDATA(k-i+1)
end
cfadd = 0
for i = 2:size(cf,"*")
cfadd = cfadd + cf(i)*yhat(k-i+1)
end
yhat = [yhat; [ bdadd + fc_dadd - cfadd ]];
end
endfunction
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