how to connect Fuzzy inference system with ANFIS

Question

ciptya almira 2017-7-18

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/349267-how-to-connect-fuzzy-inference-system-with-anfis

helloo i need your help for my case i have to build fuzzy inference system at the command line like this

[System]
Name='myfile'
Type='sugeno'
Version=2.0
NumInputs=2
NumOutputs=1
NumRules=4
AndMethod='min'
OrMethod='max'
ImpMethod='prod'
AggMethod='max'
DefuzzMethod='centroid'
[Input1]
Name='x1'
Range=[3 9]
NumMFs=2
MF1='rendah':'gbellmf',[1.2 2.5 3]
MF2='tinggi':'gbellmf',[1.2 2.5 9]
[Input2]
Name='x2'
Range=[1000 3000]
NumMFs=2
MF1='apresiasi':'gbellmf',[400 2.5 1000]
MF2='depresiasi':'gbellmf',[400 2.5 3000]
[Output1]
Name='output'
Range=[1000 3000]
NumMFs=4
MF1='line1':'linear',[0 0 0]
MF2='line2':'linear',[0 0 0]
MF3='line3':'linear',[0 0 0]
MF4='line4':'linear',[0 0 0]
[Rules]
1 0, 1 (1) : 1
2 0, 2 (1) : 1
1 1, 3 (1) : 1
2 2, 4 (1) : 1

and i want to connect them with my anfis code like this

   function [c,a,R,y,yr,coef,Et] = anhfis(A,yTarget,klas, lr, mc, maxEpoch, Eps);
[a,c,U,obj_fcn] = findDevMean(A, klas)
E = 1;
epoh = 0;
[n,m] = size(A);
y = yTarget
while (epoh < maxEpoch & E>Eps),
    epoh = epoh+1;
    Mu = zeros(n,klas,m)
    E=0;
    %lapisan 1,2,3
    coef = [];
   for i=1 : n,
       for k=1 : klas,
            w1(k) = 1;
            for j = 1 : m,
                mf (k,j) = 1/(1+((A(i,j)-c(k,j))/a(k,j))^2);
                We1(i,k,j) = w1(k)*mf(k,j);
                Mu(i,k,j) = mf(k,j)
            end;
            if m>1,
                We2(i,k) = We1(i,k,1)*We1(i,k,2);
            else
                We2 = We1;
           end;
       end;
       for k=1 : klas,
           We3(i,k) = We2(i,k)/sum(We2(i,:));
       end;
       cc=[];
       for k=1 : klas,
           cc = [cc We3(i,k)*A(i,:) We3(i,k)];
       end;
       coef = [coef; cc];
   end;
   %lapisan 45
   youtput =[]
   for i=1:n,
       for k=1:klas,
       for j=1:m,
           We4(i,k) = ((We3(i,k)*A(i,j))*coef(i,k+0)) + coef(i,k+1)
       end;
   end;
   for j=1:m,
       youtput(i,j) = sum(We4(i,:))/A(i,j)
   end;
   end;
   X = coef
   R = rekursif_LSE(X,y)
   yr = X*R
   nn1 = fix(length(R)/klas)
   for i =1:n,
       for k=1:klas,
           yt(i,k) = X(i,(k-1)*nn1+1:k*nn1)*R((k-1)*nn1+1:k*nn1)
       end;
   end;
%propagasierror
   for i=1:n,
       E5 = -2*(y(i)-yr(i))
       for k=1:klas,
           E4(i,k) = E5
       end;
       for k=1:klas,
           E3(i,k) = yt(i,k)*E4(i,k)
       end;
       for k=1:klas,
           tt=0;
           for t=1:klas,
               for j=1:m,
                   if t~=k,
                     tt = tt+We1(i,t,j)
                   end;
               end;
           end;
           if m>2
               E2(i,k)= tt/(sum(We1(i,:,1))^2)*E3(i,k)
           else
               E2(i,k)= tt/(sum(We1(i,:,k))^2)*E3(i,k)
           end;
               for t=1:klas,
                if t~=k,
                if m>2
                    E2(i,k) = E2(i,k)-(tt/(sum(We1(i,:,1))^2))*E3(i,t)
                else
                    E2(i,k) = E2(i,k)-(tt/(sum(We1(i,:,k))^2))*E3(i,t) 
                end;
               end;
           end;
   end;
           for j=1:m,
               for k=1:klas,
                   tt =1
                   if m>1,
                   for t=1:m,
                    if t~=j,
                       tt = tt*Mu(i,k,t)
                     end;
                   end;
                   else
                       tt = tt*Mu(i,k,t)
                   end;
                   E1(j,k) = tt*E2(i,k)
                   end;
               end;
               %perubahan bobot a dan c(da & dc)
               for j=1:m,
                   for k=1:klas,
                       L = A(i,j)-c(j,k)
                       H = (1+(L/a(j,k))^2)^2
                       da(j,k) = 2*(L^2)/((a(j,k)^3)*H)*E1(j,k)*lr*A(i,j)
                       dc(j,k) = 2*L/((a(j,k)^2)*H)*E1(j,k)*lr*A(i,j)
                       if epoh>1,
                           da(j,k) = mc*dalama(j,k)+(1-mc)*da(j,k)
                       end;
                       if epoh>1,
                           dc(j,k) = mc*dclama(j,k)+(1-mc)*dc(j,k)
                       end;
                       a(j,k) = a(j,k)+ da(j,k)
                       c(j,k) = c(j,k) + dc(j,k)
                       dclama(j,k) = dc(j,k)
                       dalama(j,k) = da(j,k)
                   end;
               end;
               %hitung sse
               E = E + (y(i)-yr(i))^2
               chkRMSE = norm(yr(i)-y(i))/sqrt(length(yr(i)));
               %mean(E)   % Mean Squared Error
               %RMSE = sqrt(mean(E));  % Root Mean Squared Error
       end;
       Et(epoh) = E
       rmse(epoh) = chkRMSE
       plot(1:epoh,Et);
       title(['Epoh ->' int2str(epoh)]);
       handles = guidata( findobj(0, 'tag', 'sse'));
       Sse = num2str(Et(epoh));
       set(handles.sse,'string',Sse);
       handles = guidata( findobj(0, 'tag', 'rmse'));
       RMSE = num2str(rmse(epoh))
       set(handles.rmse,'string',RMSE);
       grid
       pause(0.001);
end;
hasil = [y yr y-yr]
setappdata(0,'gethasil',hasil)
myicon = imread('checklist.png');
h=msgbox('proses pembelajaran selesai','alhamdulillah','custom',myicon);