MATLAB: Pattern Recognition with Perceptron

annDeep Learning Toolboxpattern recognitionperceptron

Hi, all
I have six patterns as shown below
A1 = [ 0 0 1 1 0 0 0;
       0 0 0 1 0 0 0;
       0 0 0 1 0 0 0;
       0 0 1 0 1 0 0;
       0 0 1 0 1 0 0;
       0 1 1 1 1 1 0;
       0 1 0 0 0 1 0;
       0 1 0 0 0 1 0;
       1 1 1 0 1 1 1];
B1 = [ 1 1 1 1 1 1 1;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 1;
       1 1 1 1 1 1 1;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 1;
       1 1 1 1 1 1 1];
C1 = [ 0 0 1 1 1 1 1;
       0 1 0 0 0 0 1;
       1 0 0 0 0 0 0;
       1 0 0 0 0 0 0;
       1 0 0 0 0 0 0;
       1 0 0 0 0 0 0;
       1 0 0 0 0 0 0;
       0 1 0 0 0 0 1;
       0 0 1 1 1 1 0];
A2 = [ 0 0 0 1 0 0 0;
       0 0 0 1 0 0 0;
       0 0 0 1 0 0 0;
       0 0 1 0 1 0 0;
       0 0 1 0 1 0 0;
       0 1 0 0 0 1 0;
       0 1 1 1 1 1 0;
       0 1 0 0 0 1 0;
       0 1 0 0 0 1 0];
B2 = [ 1 1 1 1 1 1 0;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 1;
       1 1 1 1 1 1 0;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 1;
       1 1 1 1 1 1 0];
C2 = [ 0 0 1 1 1 0 0;
       0 1 0 0 0 1 0;
       1 0 0 0 0 0 1;
       1 0 0 0 0 0 0;
       1 0 0 0 0 0 0;
       1 0 0 0 0 0 0;
       1 0 0 0 0 0 1;
       0 1 0 0 0 1 0;
       0 0 1 1 1 0 0];
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
. # # # # # .   
. # . . . # .   
. # . . . # .   
# # # . # # #
How do I do that?
Thanks in advance!
Network type is perceptron

Best Answer

Hi, Benard
A perceptron can be created with the newp function.
Eq: net = newp(PR,S,TF,LF);
- PR is m x 2 matrix of [min max] of network input
- S is a scalar that indicates number of target/pattern
- TF is transfer function. Default is 'hardlim'
- LF is learning function. Default is 'learnp'
In order to divide your data into 2 groups, you need to select A1, B1, C1 as network input.
p = [A1(1:end); B1(1:end); C1(1:end)]';
And to create the network target
t = eye(3);
Then, use train to perform network training. You can also set the network epoch by :
net.trainParam.epochs = 10;
So, the complete code is :
p = [A1(1:end); B1(1:end); C1(1:end)]';
t = eye(3);
PR = zeros(63,2);
PR(:,2) = 1;
net = newp(PR,3,'hardlim','learnp');
net.trainParam.epochs = 10;
net = train(net, p, t);
And about how to test your network just use sim command.
a = sim(net, A2(1:end)');
Now, do testing for A1 and A2. Both of them will display :
>> a = sim(net, A2(1:end)')
a =
       1
       0
       0
>> a = sim(net, A1(1:end)')
a =
       1
       0
       0
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