datavala.m

王小平《遗传算法——理论、应用与软件实现》随书光盘

function a_filt=datavala(alpha,temp,D);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%     Ref:    "Succeed at On-Line Validation and Reconstruction of Data"
%             Ming T. Tham & Andrew Parr., Measurement and Control, 1994
%             Chemical Engineering Progress.
%
%     Written by A. Mitchell 15/1/96.
%
%     n - the number of past values used in sequence is set by user,
%     n=10
%     BetaVal = 2
%     DVal = 3
%     GammaVal = 2
%     for best performance
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

a_filt=[];
n=10;
BetaVal=2;
DVal=3;
GammaVal=2;


for q=1:1:D

%
%	initialize
%

Suma1=0;
Suma2=0;
S1=0;
S2=0;
SumDelta=0;
SumMed=0;
SumaBar=0;
SumStand=0;

	if q > n+2
	a=[temp(q),a(1:n+1)];

%
%     Calculates values needed to find a distinct trend
%

	ZOld = a(2) - a(3);
	if ZOld > 0
   		SgnOld = 1;
	elseif ZOld < 0
   		SgnOld = -1;
	else
   		SgnOld = 0;
	end

	ZNew = a(1) - a(2);
	if ZNew > 0
		SgnNew = 1;
	elseif ZNew < 0
   		SgnNew = -1;
	else
   		SgnNew = 0;
	end

%
%     Calculates values needed to find a trend which isn't apparent
%

	for i=1:1:n/2
    		Suma1 = Suma1 + a(i);
	end
	aBar1 = (2/n)*(Suma1);
	for i=1+n/2:1:n
    		Suma2 = Suma2 + a(i);
	end
	aBar2 = (2/n)*(Suma2);
	for i=1:1:n/2           
    		S1 = S1 + ((a(i) - aBar1)^2 / ((n/2) - 1));
	end
	for i=1+n/2:1:n 
    		S2 = S2 + ((a(i) - aBar2)^2 / ((n/2) - 1));
	end
      
	S = sqrt((S1 + S2) / 2);
	Test = ((aBar1 - aBar2)*sqrt(n)) / (2*S);

%
%     Tests for a distinct trend
%
      
	if SgnNew == SgnOld

%
%     Deals with oultiers for a distinct trend
%

		Delta = abs(ZNew);      
		for i=1:1:n
       			SumDelta = SumDelta + abs(a(i+1) - a(1+i+1));
   		end
   		DeltaBar = (1/n)*(SumDelta)*(BetaVal);
   		if Delta > DeltaBar
      		filty = a(2) + (SgnNew)*(DeltaBar);
		else
      		filty = a(1);
   	end
        
%
%     Tests for trend that isn't apparent - value of 3.355 is a students 
%     t-test for alpha = 0.005 and n = 10.
%        

	elseif Test > 3.355

%
%     Deals with outliers for a none apparent trend
%

   		aMed = median(a(:,2:n));
      
		for i=1:1:n
			SumMed = SumMed + abs(a(1+i) - aMed);
		end
		DeltaMed = (1/n)*(SumMed)*(DVal);

		Med = (a(1) - aMed);
		if Med > 0
			SgnMed = 1;
		elseif Med < 0
			SgnMed = -1;
		else
			SgnMed = 0;    
		end
          
		if abs(Med) > DeltaMed
			filty = aMed + (SgnMed)*(DeltaMed);
		else
			filty = a(1); 
		end  
        
%
%     Otherwise No Trend
%

	else      

%
%     Deals with outliers for no trend
%

		for i=1:1:n
			SumaBar = SumaBar + a(1+i);
		end                                                                         
		aBar = (1/n)*(SumaBar);          
		for i=1:1:n-1
       		SumStand = SumStand + ((a(1+i) - aBar)^2) / (n - 1);
		end   
		Stand = (GammaVal)*(sqrt(SumStand));
        
		Bar = (a(1) - aBar);
		if Bar > 0
			SgnBar = 1; 
		elseif Bar < 0
			SgnBar = -1;
		else
			SgnBar = 0;    
		end
        
		if Bar > Stand
			filty = aBar + (SgnBar)*(Stand);
		else
			filty = a(1);
		end 
	end

	temp1(q)=filty;
	else
	temp1(q)=temp(q);
	a(n-q+1+2)=temp(q);
	end
end

temp1_filt=bdfiltb(alpha,temp1);

a_filt=temp1_filt;

return