matrix dimensions must agree

Please help me i have a problem with my program neural network about matrix. What program should I add that the error disappear??

clc

clear all

%==========================================================================

% PENGOLAHAN CITRA

%==========================================================================

%histogram

jlhkelas = 3;

jlh = 20;

dir1 = 'training\';

%dir2 = 'testing\';

for kelas = 1:jlhkelas

for byk = 1:jlh

dir = strcat(dir1,int2str(kelas),'-',int2str(byk),'.jpg');

img=imread(dir);

imgred = img(:,:,1);

imggreen = img(:,:,2);

imgblue = img(:,:,3);

[row col dim]= size(img);

imgb = ones(row+2,col+2,3);

imgb = im2uint8(imgb);[row col dim]= size(img);

imgb = ones(row+2,col+2,3);

imgb = im2uint8(imgb);

for i = 1:row

    for j = 1:col

        imgb(i+1,j+1,1) = imgred(i,j);

        imgb(i+1,j+1,2) = imggreen(i,j);

        imgb(i+1,j+1,3) = imgblue(i,j);

    end

end

%mengatur kecerahan gambar

OUTPUT = strcat(dir1,int2str(kelas),'-',int2str(byk),'.jpg');

a=rgb2gray(img);

a = imadjust(a,stretchlim(a),[]);

[row col] = size(a);

x = ones(row+2,col+2);

x = im2uint8(x);

for i =1:row

    for j=1:col

        x(i+1,j+1)= a(i,j);

    end

end

%untuk mengubah gambar ke dalam bentuk nilai biner

gray=rgb2gray(img);

thresh=graythresh(gray);

imbw=im2bw(gray,thresh);

end

end

%==========================================================================

% PENENTUAN FITUR DARI DAUN KAKAO SEBAGAI INPUT KE JST

%==========================================================================

%Grayscale

baris = 1

for i = 1:jlhkelas

for j = 1:jlh

dir = strcat(dir1,int2str(i),'-',int2str(j),'.jpg');

I = imread(dir);

I = rgb2gray(I);

Ih = imhist(I);

Ih = Ih(20:220);

Ih(Ih == 0) = [];

%Mean

rata2 = mean(Ih);

%Standar Deviasi

sdeviasi = std(Ih);

%Kurtosis

K = kurtosis(Ih);

%Skewness

Sk = skewness(Ih);

[p,SI] = graycomatrix(I,'NumLevels',max(max(I))+1,'G',[]);

p(p==0) = [];

p = p/sum(p);

%Entropy

E = -sum((p).*log2(p));

MGray(baris,:) = [rata2 sdeviasi K Sk E];

%RGB Normalisasi

Image_rgb = imread(dir);

Image_rgb = double(Image_rgb);

Image_red = Image_rgb(:,:,1);

Image_green = Image_rgb(:,:,2);

Image_blue = Image_rgb(:,:,3);

[row,col] = size(Image_rgb(:,:,1));

 for y = 1:row %-->numberof rows in image

     for x = 1:col %-->number of columns in the image

                 Red = Image_red(y,x);

                 Green = Image_green(y,x);

                 Blue = Image_blue(y,x);

                 NormalizedRed   = Red  /sqrt(Red^2 + Green^2 + Blue^2);

                 NormalizedGreen = Green/sqrt(Red^2 + Green^2 + Blue^2);

                 NormalizedBlue  = Blue /sqrt(Red^2 + Green^2 + Blue^2);

                 Image_red(y,x) = NormalizedRed;

                 Image_green(y,x) = NormalizedGreen;

                 Image_blue(y,x) = NormalizedBlue;

     end

 end

             Image_rgb(:,:,1) = (Image_red);

             Image_rgb(:,:,2) = (Image_green);

             Image_rgb = Image_rgb(:,:,1).*Image_rgb(:,:,2);

             Img = im2uint8(Image_rgb);   

             Imgb = im2double(Img);

             Imgb = imhist (Imgb);

             Imgb(Imgb==0) = [];

%MeanRGB

rata2RGB = mean(Imgb);

%Standar DeviasiRGB

sdeviasiRGB = std(Imgb);

%KurtosisRGB

KRGB = kurtosis(Imgb);

%SkewnessRGB

SkRGB = skewness(Imgb);

[p1,SI] = graycomatrix(Img,'NumLevels',max(max(Img))+1,'G',[]);

p1(p1==0) = [];

p1 = p1/sum(p1);

%EntropyRGB

ERGB = -sum((p1).*log2(p1));

MRGB(baris,:) = [rata2RGB sdeviasiRGB KRGB SkRGB ERGB];

%Pengaturan Kecerahan Image

a = I;

a = imadjust(a,stretchlim(a),[]);

[row col] = size(a);

x = ones(row+2,col+2);

x = im2uint8(x);

for in =1:row

    for jn=1:col

x(in+1,jn+1)= a(in,jn);

    end

end

%Level Saturasi4

Image_Sat = imread(dir);

hsv = rgb2hsv(Image_Sat);

s = hsv(:,:,2);

s = im2uint8(s);

shist = imhist(s);

shist = shist(20:220);

shist(shist == 0) = [];

%===========MeanSat

rata2Sat = mean(shist);

%===========Standar DeviasiSat

sdeviasiSat = std(shist);

%===========KurtosisSat

KSat = kurtosis(shist);

%===========SkewnessSat

SkSat = skewness(shist);

%===========EntropySat

[p3,SI] = graycomatrix(s,'NumLevels',max(max(s))+1,'G',[]);

p3(p3==0) = [];

p2 = p3/sum(p3);

ESat = -sum((p3).*log2(p3));

MSat(baris,:) = [rata2Sat sdeviasiSat KSat SkSat ESat];

baris = baris +1;

end

end

Hasil = [(1:baris-1)' MGray MRGB MSat];

P = ([MGray MRGB MSat])';

P = (P)/1e+002

T =[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1];

% Preprocessing

[pn,meanp,stdp,tn,meant,stdt] = prestd(P,T);

% Membangun jaringan dengan propagasi umpan balik berbasis jaringan syaraf tiruan

net = newff(minmax(pn),[10 5 1],{'tansig' 'logsig' 'purelin'},'traingdm')

% melihat bobot awal jaringan

BobotAwal_Input = net.IW{1,1}

BobotAwal_Bias_Input = net.b{1,1}

BobotAwal_Lapisan1 = net.LW {2,1}

BobotAwal_Bias_Lapisan = net.b {2,1}

BobotAkhir_Lapisan2 = net.LW {3,2}

BobotAkhir_Bias_Lapisan2 = net.b{3,1}

% Set parameter pelatihan

net.trainParam.epochs = 1000;

net.trainParam.goal = 1e-04;

net.trainParam.lr = 0.6;

net.trainParam.showCommandLine = true;

net.trainParam.show = 100;

net.trainParam.mc = 0.7;

% Pelatihan

net = train(net,pn,tn);pause

% Melihat bobot akhir jaringan

BobotAkhir_Input = net.IW{1,1}

BobotAkhir_Bias_Input = net.b{1,1}

BobotAkhir_Lapisan1 = net.LW {2,1}

BobotAkhir_Bias_Lapisan = net.b {2,1}

BobotAkhir_Lapisan2 = net.LW {3,2}

BobotAkhir_Bias_Lapisan2 = net.b{3,1}

% Simulasi terhadap data pelatihan

an = sim(net,pn);

a = poststd(an,meant,stdt);

H = [(1:size(P,2))' T' a' (T'-a')];

sprintf('%2d %9.2f %7.2f %5.2f\n',H')

% Menggambar Grafik

[m1,a1,r1] = postreg(a,T)

pause;

plot ([1:size(P,2)]' ,T,'bo',[1:size(P,2)]',a','r*');

title('Hasil pengujian dengan data pelatihan Target (o) dan Output (*)');

xlabel('Data ke-');

ylabel('Target atau Output');

grid;

pause

%Input Data Baru

%Cek =load( 'TData.mat')

%Cek =load ('TData.mat','Q')

%Test =Q(:,1:30)';

%Q = 'TData.mat{(:,1:30)}';

Q = xlsread('data.xlsx');

TQ =[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1];

%Normalisasi input baru

Qn = trastd(Q,meanp,stdp);

bn = sim(net,Qn);

b = poststd(bn,meant,stdt);

L = [(1:size (Q,2))' T' b' (TQ' -b')];

sprintf('%2d %11.2f %9.2f %7.2f\n', L')

%Evaluasi output jaringan (data testing dengan target)

[m2,b1,r2] = postreg(b,TQ)

pause

k = [1:size(Q,2)]';

plot(k,TQ,'bo',k,b','r*');

title('Perbandingan antara Target (o) dan Output Jaringan (*)');

xlabel('input kedua');

ylabel('Target atau Output');

grid;

text(k+0.2*ones(lenght(k),1),TQ,int2str(k));

error:

??? Error using ==> minus

Matrix dimensions must agree.

Error in ==> trastd at 75

pn = (p-meanp*oneQ)./(stdp*oneQ);

Error in ==> fix at 244

Qn = trastd(Q,meanp,stdp);

[Walter Roberson]

http://www.mathworks.com/matlabcentral/answers/13205-tutorial-how-to-format-your-question-with-markup

[Walter Roberson]

At the command prompt, command

dbstop if error

and run your program. When it stops, at the command prompt ask

size(p)
size(meanp*oneQ)

are they the same size, or is one of them a scalar?

[Een Qurota Aini]

thanks mr. Walter,but i'm not understand. please help me. help me how to find a solution to complete the program. What should I do and where I should start a program to fix it? please help me :'(

[Image Analyst]

You can also attach your m-file with the paper clip icon.

Answer 1

[Image Analyst]

First look at this: http://blogs.mathworks.com/videos/2012/07/03/debugging-in-matlab/. Then, before the pn= line, put these commands.

whos p
whos meanp
whos oneQ
whos stdp
whos oneQ

Tell us what it says in the command window.