Perceptron : Recognition Pattern ABC

Hi, all

I have six patterns as shown below

A1 = [ -1 -1  1  1 -1 -1 -1;
       -1 -1 -1  1 -1 -1 -1;
       -1 -1 -1  1 -1 -1 -1;
       -1 -1  1 -1  1 -1 -1;
       -1 -1  1 -1  1 -1 -1;
       -1  1  1  1  1  1 -1;
       -1  1 -1 -1 -1  1 -1;
       -1  1 -1 -1 -1  1 -1;
        1  1  1 -1  1  1  1];
B1 = [  1  1  1  1  1  1  1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1  1;
        1  1  1  1  1  1  1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1  1;
        1  1  1  1  1  1  1];
C1 = [ -1 -1  1  1  1  1  1 ;
       -1  1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1 -1;
        1 -1 -1 -1 -1 -1 -1;
        1 -1 -1 -1 -1 -1 -1;
        1 -1 -1 -1 -1 -1 -1;
        1 -1 -1 -1 -1 -1 -1;
       -1  1 -1 -1 -1 -1  1;
       -1 -1  1  1  1  1 -1];
A2 = [ -1 -1 -1  1 -1 -1 -1;
       -1 -1 -1  1 -1 -1 -1;
       -1 -1 -1  1 -1 -1 -1;
       -1 -1  1 -1  1 -1 -1;
       -1 -1  1 -1  1 -1 -1;
       -1  1 -1 -1 -1  1 -1;
       -1  1  1  1  1  1 -1;
       -1  1 -1 -1 -1  1 -1;
       -1  1 -1 -1 -1  1 -1];
B2 = [  1  1  1  1  1  1 -1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1  1;
        1  1  1  1  1  1 -1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1  1;
        1  1  1  1  1  1 -1];
C2 = [ -1 -1  1  1  1 -1 -1;
       -1  1 -1 -1 -1  1 -1;
        1 -1 -1 -1 -1 -1  1;
        1 -1 -1 -1 -1 -1 -1;
        1 -1 -1 -1 -1 -1 -1;
        1 -1 -1 -1 -1 -1 -1;
        1 -1 -1 -1 -1 -1  1;
       -1  1 -1 -1 -1  1 -1;
       -1 -1  1  1  1 -1 -1];

I have to recognize these patterns with artificial neural network.

I am new in Matlab. Please help!

I need to divide this data into 2 groups.

The first group A1, B1, C1 as training data. The second group A2, B2, C2 used to validate/test the network.

Example : if I select A1 then the output must display 'A', if I select B1 then the output must display 'B', if I select A2 then the output must display 'A'.

. . # # . . .   
. . . # . . .   
. . . # . . .   
. . # . # . .   
. . # . # . .   => This pattern should be recognized as A
. # # # # # .   
. # . . . # .   
. # . . . # .   
# # # . # # #

In result program, we must explain epochs from start to finish , which Learning Rate = 1 And Threshold Value = 0.5

How do I do that?

Thanks in advance!

Network type is perceptron

Answer 1

[Greg Heath]

Start with MATLAB examples

 help fitnet      % regression/curve-fitting
 help patternnet  % classification/pattern-recognition
 help nndata

 Then submit failed code with comments and/or error messages

Hope this helps.

Greg

Answer 2

[Greg Heath]

9X7 input matrices have to be columnized using the (:) operator into 63 dimensional vectors. Outputs should be columns of the 3-dimensional unit matrix. In order to avoid overtraining an overfit net, a tremendous amount of variable reduction should be applied to the inputs.

[yaqdee frarie]

im not understand matlab.. its my first learn matlab.. i dont understand coding make perceptron using matlab.. please help me..