added scilabs files

1 files changed, 122 insertions, 0 deletions

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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