While running the following program error occur as " TrainOb = 228 ??? Index exceeds matrix dimensions"

%*************************************
% Begin of Program
% Set parameters 
% Clear the operating environment
clc;
clear all;
load TXY.txt;

% for testing length of TXY

LengthofInputdata=length(TXY);

% TXY;
% Input No. of Normalized Data
%  Or use mapminmax;

TrainOb=228  % No. of Traning data

% LenghtofTrain=length(OP)

P = TXY(1:TrainOb,1:7);
LenghtofTrain=length(P)

   P=P'

%  Normalized the Data

   for i9=1:length(P(:,1))

      P(i9,:)=(P(i9,:)-min(P(i9,:)))/(max(P(i9,:))-min(P(i9,:)));

   end

NP=P

LtofTrNormal=length(NP);

Ltr=length(NP);

[row,col]=size(TXY);

set=row/5;
row=row-set;
row1=row/2;

%***************************
Lth=length(TXY)
OP = TXY(1:TrainOb,1:7);
LenghtofTrain=length(OP)
NP=NP'

% for testing length of traindata1
traindata1=NP(1:row1,1:col-1);

% length(traindata1);
% for testing length of traindata2

traindata2=NP(row1+1:row,1:col-1);

%length(traindata2);
% target of traindata1

t1=NP(1:row1,col);

% target of traindata2

t2=NP(row1+1:row,col);
t1=t1'
t2=t2'
tr1=traindata1'
tr2=traindata2'

la=1;
X_axis=rand();
Y_axis=rand();
maxgen=100;  
% maxgen=50;  
sizepop=10; 
%*********
for i=1:sizepop

X(i)=X_axis+20*rand()-10;
Y(i)=Y_axis+20*rand()-10;
D(i)=(X(i)^2+Y(i)^2)^0.5;
S(i)=1/D(i);

%***

g=0;
p=S(i); % Learning spread of GRNN

if 0.001>p
  p=1;
end

% Cross validation

if la == 1

    net=newgrnn(tr1,t1,p);
    yc=sim(net,tr2);

    y=yc-t2;%

    for ii=1:row1
      g=g+y(ii)^2;
    end

Smell(i)=(g/row1)^0.5; % RMSE

la=2;

   else

    net=newgrnn(tr2,t2,p);
    yc=sim(net,tr1);

    y=yc-t1;%

    for ii=1:row1
      g=g+y(ii)^2;
    end

    Smell(i)=(g/row1)^0.5; % RMSE

la=1;

   end

end

%***

[bestSmell bestindex]=min(Smell);
X_axis=X(bestindex);
Y_axis=Y(bestindex);
bestS=S(bestindex);
Smellbest=bestSmell;
%

for gen=1:maxgen

gen 

bestS

    for i=1:sizepop

   %

    g=0;

    X(i)=X_axis+20*rand()-10;
    Y(i)=Y_axis+20*rand()-10;

    %

    D(i)=(X(i)^2+Y(i)^2)^0.5;

    %

    S(i)=1/D(i);

    %
    p=S(i); % Learning the spread of GRNN

    if 0.001>p
    p=1;
    end

% Cross validation

if la == 1
  net=newgrnn(tr1,t1,p);
  yc=sim(net,tr2);

    y=yc-t2;%

    for ii=1:row1
      g=g+y(ii)^2;
    end

    Smell(i)=(g/row1)^0.5;  % RMSE

la=2;

   else

    net=newgrnn(tr2,t2,p);
    yc=sim(net,tr1);

    y=yc-t1;

    for ii=1:row1
      g=g+y(ii)^2;
    end

    Smell(i)=(g/row1)^0.5;  

    la=1;

   end

end

    %***

    [bestSmell bestindex]=min(Smell); % find the min of RMSE

    %***
     if bestSmell<Smellbest

           X_axis=X(bestindex);
           Y_axis=Y(bestindex);
           bestS=S(bestindex);
           Smellbest=bestSmell;
     end

     %

     yy(gen)=Smellbest; 
     Xbest(gen)=X_axis;
     Ybest(gen)=Y_axis;
  end

%

figure(1)

plot(yy)

title('Optimization process','fontsize',12)
xlabel('Iteration Number','fontsize',12);ylabel('RMSE','fontsize',12);

bestS
Xbest
Ybest

figure(2)

plot(Xbest,Ybest,'b.');
title('Fruit fly flying route','fontsize',14)
xlabel('X-axis','fontsize',12);ylabel('Y-axis','fontsize',12);
%*******Begin to Predict

% TestData

LengthofInputdata=length(TXY)

% Input No. of Normalized Testing Data 
% LenghtofAll=length(OP)

P = TXY(1:LengthofInputdata,1:7);

% LenghtofTallData=length(P);
% Length of testing data (All Data Normalized)
% Changed Non-normalized Data into Normalized Data

P=P';

for i9=1:length(P(:,1))
  P(i9,:)=(P(i9,:)-min(P(i9,:)))/(max(P(i9,:))-min(P(i9,:)));
end

Nt=P';

% Training Data

TrainData=Nt(1:row,1:col-1);
tr=TrainData';

% tr=[tr1 tr2]
% LTr=length(tr)
% Testing Data

TestData=Nt(row+1:LengthofInputdata,1:col-1);

% predict value of testdata
% No target Y

test3=TestData';
LengthofTestData=length(TestData)
t3=TXY(row+1:LengthofInputdata,col);

% length_tr3=length(tr3);
% tt=Nt(1:row,col);

tt=[t1 t2];

% Ltt=length(tt)
% bestS for parameter p;

p=bestS;

% TrainData put inot grnn

net=newgrnn(tr,tt,p);

%%predict value of testdata

ytest=sim(net,test3);
Y_hat=ytest'

% length_Y_hat=length(Y_hat)
% Predicted output Y_hat normalized
Lny=length(Y_hat);
P = Y_hat(1:Lny,1);
P=P';
LenghtofTrain=length(P)

% Changed Non-normalized Data into Normalized Data

for i9=1:length(P(:,1))

    P(i9,:)=(P(i9,:)-min(P(i9,:)))/(max(P(i9,:))-min(P(i9,:)));

end

   NPP=P';

% target of testdata
Target3=t3;

save Y_hat

% End of Program

DATA:

TXY.txt:

-0.14 13.21 67.57 -102.79 7 0 0
-0.77 8.24 -45.01 -102.04 7 0 1
0.63 23.86 -44.33 -33.27 4 0 1
-1.55 23.73 14.95 128.27 7 1 0
1.71 44.32 10.13 8.38 7 0 1
0.86 18.03 -11.6 9.72 5 0 1
-3.36 16.97 66.23 80.83 10 0 0
2.16 11.71 -19.3 -30.35 6 0 1
2.19 18.19 -1.61 20 7 0 1
-5.12 3.51 40.61 2.75 10 0 0
0.26 7.05 -12.44 -76.7 4 0 1
0.94 39.03 -3.53 -29.26 4 0 1
-3.45 12.1 28.25 98.66 9 1 0
0.26 7.05 -12.44 -76.7 4 0 1
-1.64 9.97 17.93 68.33 6 0 1
-30.54 2 -75.62 -304.66 10 0 0
-1.64 9.97 17.93 68.33 6 0 1
0.89 33.97 -1.11 -46.5 8 0 1
8.5 12.49 92.58 20.64 10 0 0
-2.91 3.55 -66.12 -2280.87 6 0 1
13.27 11.2 6.47 11.86 5 0 1
-45.41 -14.41 48.3 -34.01 10 0 0
0.58 8.05 0.73 -58.02 5 0 1
-2.91 3.55 -66.12 -2280.87 6 0 1
-34.97 8.62 68.62 -109.11 9 1 0
6.86 60.46 11.41 87.51 5 0 1
5.18 36.69 11.8 265.44 7 0 1
-22.13 3.89 48.62 -35.67 9 0 0
2.16 11.71 -19.3 -30.35 6 0 1
2.19 18.19 -1.61 20 7 0 1
4.62 7.75 439.85 232.22 7 0 0
6.61 17.28 118.89 742.37 6 0 1
1.34 10.6 -8.63 -57.62 6 0 1
-3.07 1.57 -45.41 -86.28 10 6 0
0.26 9.3 10.29 -132.6 6 0 1
5.21 27.9 12.24 -9.6 2 0 1
-10.95 3.18 -35.88 1.17 8 0 0
2.05 3.99 -68.34 -131.13 6 0 1
0.73 14.35 0 -67.23 5 0 1
-4.69 8.57 24.27 -309.74 9 2 0
3.11 10.75 -48.55 -50.82 3 0 1
0.86 15.2 0.73 47.41 5 0 1
-11.21 13.33 -34 -369.86 10 0 0
-0.23 -0.03 -102.15 110.51 7 0 1
-0.33 9.36 272.19 -24.36 8 0 1
-5.61 100 -94.89 31.85 10 0 0
-0.19 5.47 -38.36 -50.19 6 0 1
0.74 8.07 -5.47 50.09 6 0 1
-5.91 -18.23 -951.11 57.34 9 0 0
-2.01 5.99 396.44 42.35 6 0 1
1.9 8.77 -49.71 -53.56 7 0 1
-11.4 5.57 -42.36 2.26 9 0 0
9.78 20.43 22.84 34.61 5 0 1
4.05 14.52 -52.99 -48.17 6 0 1
-12.05 -2.43 -160.22 -177.48 10 0 0
6.5 14.47 -18.25 28.53 3 0 1
2.63 12.07 3.13 224.81 6 0 1
-1.74 3.29 -43.77 -1601.44 9 3 0
0.27 -1.01 -118.66 -94.51 9 0 1
0.26 9.3 10.29 -132.6 6 0 1
-3.83 -2.34 -125.1 -142.15 6 0 0
-0.33 9.36 272.19 -24.36 8 0 1
-8.86 25.18 29.31 18.42 7 0 1
-1.9 7.97 207.03 -128.86 8 0 0
0.73 14.35 0 -67.23 5 0 1
2.05 3.99 -68.34 -131.13 6 0 1
2.29 15.66 -14.89 -24.02 5 0 0
0.33 9.07 6.08 -19.98 6 0 1
0.29 13.72 689.79 117.49 6 0 1
-27.99 6.43 -60.04 -15272.03 10 2 0
1.9 8.77 -49.71 -53.56 7 0 1
0.07 14.11 -7.87 178.5 7 0 1
-21.1 16.08 -23.44 -2686.34 9 0 0
9.4 19.04 -2.74 11.06 4 0 1
6.95 11.54 36.56 53.24 4 0 1
-23.51 7.39 23.31 -155.29 8 6 0
1.65 11 263.39 120.71 5 0 1
3.26 12.4 39.14 1806.34 6 0 1
-0.2 13.58 -16.87 -97.18 8 5 0
9.4 19.04 -2.74 11.06 4 0 1
0.82 20.92 30.78 59.47 5 0 1
-3.93 9.06 18.53 -1153.33 10 0 0
1.6 8.34 60.64 257.58 7 0 1
-3.35 1.7 -74.91 -217.91 7 0 1
-10.76 6.24 16.23 -221.14 10 0 0
9.82 17.49 -9.26 -53.57 5 0 1
1.84 5.66 7.26 -26.04 9 0 1
1.23 9.95 -7.19 -74.09 5 1 0
11.53 23.88 17.4 35.92 3 0 1
3.71 18.67 -28.65 -37.32 4 0 1
-22.62 -5.58 -150.28 -217.77 9 0 0
3.75 18.38 -3.5 19.14 6 0 1
0.83 23.69 -1.18 -79.52 6 0 1
7.01 35.21 3.01 -3.87 5 0 0
4.94 25.39 -30.9 -42.45 2 0 1
0.83 23.69 -1.18 -79.52 6 0 1
-13.23 2 -87.55 -52.61 10 0 0
3.75 18.38 -3.5 19.14 6 0 1
0.83 23.69 -1.18 -79.52 6 0 1
-0.55 10.5 -3.12 -184.86 9 0 0
0.67 10.75 -13.25 -71.05 5 0 1
1.58 8.81 -16.63 47.63 5 0 1
-6.81 -4.39 -148.74 -4639.23 8 0 0
0.6 12.04 -24.77 -82.12 7 0 1
0.67 10.75 -13.25 -71.05 5 0 1
0.97 8.86 35.19 -64.56 10 1 0
0.74 0.33 -97.31 -50.44 9 0 1
4.32 23.64 115.67 158.93 6 0 1
-0.35 5.52 -38.9 -43.59 6 0 0
18.77 21.24 70.69 213.78 5 0 1
3.24 8.15 -23.34 -29.07 5 0 1
0.18 10.01 -15.56 -82.76 5 0 0
0.6 12.04 -24.77 -82.12 7 0 1
1.58 8.81 -16.63 47.63 5 0 1
-3.45 -0.95 -126.29 -50.41 9 0 0
0.8 10.22 -53.07 -77.37 5 0 1
0.55 7.6 -51.53 -91.71 8 0 1
-6.44 2.01 -61.36 -199.43 10 0 0
1.96 12.34 18.85 -6.92 3 0 1
0 17.15 48.18 100.28 9 0 1
8.15 17.44 -19.84 497.17 10 2 0
-1.35 11.25 -34.31 -464.82 6 0 1
2 14.1 -26.74 -73.15 5 0 1
2.61 8.84 -14.93 -68.16 5 2 0
10.13 13.78 20.29 4.35 2 0 1
9.6 7.14 14.44 71.97 3 0 1
-10.79 -13.05 8.29 -21.44 7 0 0
1.4 30.21 -16.22 -47.81 3 0 1
4.31 19.86 10.42 7.25 4 0 1
-14.21 -4.6 -268.95 -61.2 7 0 0
-11.58 5.32 653 32.06 7 0 1
12.3 13.86 -2.71 -29.84 5 0 1
-48.86 -0.29 -126.34 -76.73 10 0 0
3.97 9.21 -44.05 -66.77 6 0 1
5.94 10.9 -11.1 -51.3 5 0 1
-17.76 15.48 -35.12 -535.39 7 0 0
1.4 30.21 -16.22 -47.81 3 0 1
18.11 29.29 -14.81 -42.33 3 0 1
-1.88 0.58 371.14 71.68 8 0 0
3.74 31.32 -29.26 -18.48 7 0 1
7.12 23.44 -5.04 -5.85 4 0 1
-82.48 -3.44 -251.13 -336.18 9 0 0
12.4 13.4 74.63 463.66 3 0 1
-4.94 11.82 7.5 -156.64 3 1 1
-8.19 -14.03 -270.09 -58.22 8 0 0
-1.53 5.99 32.02 67.99 4 0 1
3.99 22.05 108.86 169.04 5 0 1
-78.39 6.11 -72.05 -1831.99 10 1 0
10.14 41.46 -24.61 -24.72 7 0 1
8.79 23.73 75.89 97.87 5 0 1
-52.33 -1.08 -120.7 -359.6 10 0 0
6 23.17 45.78 22.32 6 0 1
-0.8 4.75 -38.12 -108.25 5 0 1
-4.21 10.76 -22.84 -761.79 7 0 0
1.63 24.41 59.22 2597.98 6 0 1
4.14 16.34 -6.9 -29.69 4 0 1
-3.06 11.1 -5.34 -95.63 7 0 0
15.45 19.54 77 114.63 3 0 1
1.33 13.54 -13.37 -9.4 5 0 1
-1.22 27.46 -24.06 -491.9 10 10 0
0.5 15.15 42.14 413.46 6 0 1
2.51 9.69 -40.47 -70.48 6 0 1
-14.06 64.54 -38.98 -881.36 10 0 0
5.26 45.49 5.86 58.66 4 0 1
1.48 82.08 8.9 21.19 5 0 1
-2.69 11.16 13.22 89.63 9 2 0
-1.51 16.49 18.28 -190.98 9 0 1
3.72 31.63 41.32 1404.3 4 0 1
-90.57 23.43 -28.73 -3899.68 10 3 0
13.36 20.05 107.41 213.94 7 0 1
4.41 18.59 11.08 156.87 6 0 1
-9.57 12.83 36.14 17.88 10 4 0
5.05 91.28 -0.45 3.36 5 0 1
-0.83 20.98 -22.46 -148.87 6 0 1
-14.61 0.2 -92.89 56.24 8 0 0
4.75 27.37 -0.39 -42.92 5 0 1
4.11 9.02 -3 5.82 4 0 1
-9.99 2.16 -69.68 -512.26 9 0 0
-12.44 11.99 0.27 -139.13 7 0 1
6.1 18.05 41.2 39.99 5 0 1
-7.56 -3.54 -121.19 -81.83 9 0 0
3.98 12.43 0.29 -27.95 4 0 1
1.76 12.42 31.47 -45.64 6 0 1
-4.26 3.99 -43.13 -6096.46 10 0 0
3.16 18.11 11.05 73.39 5 0 1
2.54 13.65 25.38 34.8 6 0 1
126.61 -33.48 -206.6 221.24 10 0 0
-3.61 -6.13 -49.71 -54.52 7 0 1
2.63 12.07 3.13 224.81 6 0 1
-17.46 2.53 -71.83 -524.78 9 0 0
-2.01 5.99 396.44 42.35 6 0 1
4.05 14.52 -52.99 -48.17 6 0 1
-5.12 11.95 -14.64 -1380.43 10 3 0
0.29 13.72 689.79 117.49 6 0 1
0.86 6.62 647.42 -48.73 6 0 1
-29.99 3.57 -50.86 45.34 10 0 0
11.61 16.04 39.32 182.8 5 0 1
12.9 8.2 -17.84 113.1 6 0 1
-14.64 -5.48 -232.59 14.51 8 0 0
12.62 15.49 -5.19 -11.24 6 0 1
14.18 29.38 3.07 -9.58 6 0 1
17.15 17.04 0.4 213.03 5 7 0
12.25 26.81 4.38 35.03 5 0 1
5.11 23.76 -2.31 -12.48 4 0 1
-74.86 4.05 -73.43 -431.64 10 0 0
15.47 68.66 14.49 2.35 4 0 1
1.31 9.21 7.98 -57.97 6 0 1
-1.91 6.58 -26.15 -179.17 8 0 0
0.8 10.22 -53.07 -77.37 5 0 1
0.55 7.6 -51.53 -91.71 8 0 1
-7.7 3.72 -43.83 -8.05 10 0 0
0 17.15 48.18 100.28 9 0 1
1.96 12.34 18.85 -6.92 3 0 1
-17.56 7.34 -67.84 -6347.57 10 0 0
11.48 82.82 61.64 91.78 6 0 1
0.13 13.37 12.03 -86.4 7 0 1
-13.51 23.98 13.12 -8.19 9 2 0
0.47 22.13 33.29 -33.64 6 0 1
38.11 68.2 12.91 -2.83 5 0 1
-39.95 22.37 -25.11 -766.53 10 0 0
-1.03 87.61 178.9 104.31 7 3 1
-15.86 40.02 -34.32 3.35 7 0 1
-14.17 6.54 -11.38 -588 10 0 0
4.52 15.78 -37.81 -28.57 6 0 1
13.18 43.66 -8.15 2.73 6 0 1
-36.04 -49.94 -728.54 -6401.43 8 0 0
2.49 18.41 -19.18 -90.88 4 0 1
9.36 22.66 -14.66 -16.67 3 0 1
-19.33 -33.64 -22.01 56.85 6 0 0
1.42 9.67 51.56 157.68 3 0 1
-4.96 2.17 427.12 -18.29 6 0 1
-6.09 5.33 -30.31 -103.78 7 0 0
13.09 12.49 12.82 22.62 5 0 1
13.89 26.83 140.51 629.3 6 0 1
-7.79 9.37 115.07 47.13 10 0 0
1.32 31.91 -38.97 -97.94 7 0 1
4.65 31.12 15.22 251.46 6 0 1
-175.79 2.81 -83.18 -1068.06 10 0 0
-0.63 6.63 3.96 -136.77 8 1 1
4.49 17.55 -10.92 -9.95 5 0 1
-60.5 -11.72 -137.3 -47.52 9 0 0
-3.56 46.43 -8.58 -45.86 7 0 1
3.42 77.81 1.09 -30.62 4 0 1
2 20.39 20.16 -13.52 10 0 0
3.72 31.63 41.32 1404.3 4 0 1
4.56 14.7 -9.16 35.57 3 0 1
-22.98 2.97 -69.61 -341.84 8 1 0
3.67 11 -8.86 -21.02 7 0 1
3.65 34.15 -18.51 -61.79 5 0 1
-15.74 29.07 -56.53 -396.8 9 1 0
4.28 20.05 35.21 10.38 5 0 1
-0.89 11.03 920.73 29.8 8 0 1
-3.97 4.48 -36.95 -175.25 10 3 0
4.03 58.39 14.7 8.94 6 0 1
3.86 18.59 -5.45 -67.56 6 0 1
-16.36 18.63 11.29 -434.7 9 3 0
8.35 20.95 28.67 57.16 5 0 1
6.9 13.71 -21.94 -36.05 6 0 1
-55.84 8.6 -82.3 -1354.3 10 1 0
-4.6 18.7 12.8 -148.26 6 0 1
-4.41 -0.6 -89.79 -185.4 7 0 1
-28.76 -3.56 -191.68 18.83 8 0 0
10.82 18.44 29.13 51.05 7 0 1
17.39 40.03 66.22 202.89 7 1 1
20.47 14.5 150.4 237.44 9 2 0
10.44 61.72 13.64 -13.25 4 0 1
18.74 21.77 80.89 136.69 3 0 1
14.9 26 84.04 209.46 10 0 0
2.63 12.07 3.13 224.81 6 0 1
-6.25 7.19 -57.39 -301.21 8 0 1
4.49 7.27 -9.6 1293.72 9 1 0
13.36 20.05 107.41 213.94 7 0 1
4.41 18.59 11.08 156.87 6 0 1
-26.48 -2.12 -120.43 -984.43 8 0 0
2.37 5.96 119.84 -99.16 5 0 1
0 17.15 48.18 100.28 9 0 1
-7.75 14.79 -6.76 -339.86 10 3 0
-3.22 17.9 -20.21 -109.53 6 0 1
3.41 20.1 -10.41 -50.35 5 1 1
-3.54 12.81 -56.62 -127.07 10 2 0
17.69 15.42 20.61 23.52 2 0 1
10.13 13.78 20.29 4.35 2 0 1
-0.03 16.12 3.35 -95.25 8 0 0
3.41 20.1 -10.41 -50.35 5 1 1
2.99 34.78 18.48 25.64 4 0 1

[Jan]

Please format your code properly (as explained in the documentation of the forum and as discussed hundrets of times in other threads). Do you see, that the currently provided code is not readable?

Posting the relevant part of the code would allow us to concentrate on the problem without needing an hour to understand your code. The complete error message would reveal more details.

[satheeshkumar satheeshkumar M]

The relevant part of the code

% TXY;
% Input No. of Normalized Data
%  Or use mapminmax;
TrainOb=228  % No. of Traning data
% LenghtofTrain=length(OP)

The complete error message as follows

TrainOb =

     228

??? Index exceeds matrix dimensions.

Answer 1

[Walter Roberson]

At the MATLAB command prompt give the command

dbstop if error

and then run your program again. When it stops with the error, give the comman

dbstack

and show us the output of that.

[satheeshkumar satheeshkumar M]

While using dbstop if error also following statement appears TrainOb = 228 ??? Index exceeds matrix dimensions. After using dbstack found no output

[Walter Roberson]

Edit your file in the MATLAB editor at at the line

if la == 1

click on the line. Now click on "Run to Cursor". When it stops, click on "Step" repeatedly until the program fails. When it does tell us which line it failed on.

I cannot test this myself as I do not have the Neural Network toolbox.