This example uses 4 input, 6 hidden and 14 output nodes to classify 14 patterns.

In the Java version, I've introduced a noise factor which varies the original input a little, just to see how much the network can tolerate.

It generally works pretty well. The noise factor setting here is 0.45. The correct output should go in order. The noise causes the net to predict input #1 as #8.

Network is 100.0% correct.

Test network against original input:
1.0	1.0	1.0	0.0	Output: 0
1.0	1.0	0.0	0.0	Output: 1
0.0	1.0	1.0	0.0	Output: 2
1.0	0.0	1.0	0.0	Output: 3
1.0	0.0	0.0	0.0	Output: 4
0.0	1.0	0.0	0.0	Output: 5
0.0	0.0	1.0	0.0	Output: 6
1.0	1.0	1.0	1.0	Output: 7
1.0	1.0	0.0	1.0	Output: 8
0.0	1.0	1.0	1.0	Output: 9
1.0	0.0	1.0	1.0	Output: 10
1.0	0.0	0.0	1.0	Output: 11
0.0	1.0	0.0	1.0	Output: 12
0.0	0.0	1.0	1.0	Output: 13

Test network against noisy input:
1.4	1.4	1.0	0.1	Output: 0
1.1	1.2	0.4	0.4	Output: 8
0.1	1.2	1.2	0.3	Output: 2
1.3	0.3	1.3	0.4	Output: 3
1.1	0.1	0.2	0.2	Output: 4
0.2	1.2	0.3	0.3	Output: 5
0.3	0.0	1.3	0.4	Output: 6
1.1	1.3	1.2	1.4	Output: 7
1.3	1.0	0.3	1.4	Output: 8
0.0	1.2	1.4	1.1	Output: 9
1.2	0.2	1.4	1.2	Output: 10
1.3	0.1	0.3	1.4	Output: 11
0.2	1.2	0.3	1.4	Output: 12
0.3	0.4	1.4	1.4	Output: 13

It can take a little noise, but not too much. The noise factor here is 0.58.

Network is 100.0% correct.

Test network against original input:
1.0	1.0	1.0	0.0	Output: 0
1.0	1.0	0.0	0.0	Output: 1
0.0	1.0	1.0	0.0	Output: 2
1.0	0.0	1.0	0.0	Output: 3
1.0	0.0	0.0	0.0	Output: 4
0.0	1.0	0.0	0.0	Output: 5
0.0	0.0	1.0	0.0	Output: 6
1.0	1.0	1.0	1.0	Output: 7
1.0	1.0	0.0	1.0	Output: 8
0.0	1.0	1.0	1.0	Output: 9
1.0	0.0	1.0	1.0	Output: 10
1.0	0.0	0.0	1.0	Output: 11
0.0	1.0	0.0	1.0	Output: 12
0.0	0.0	1.0	1.0	Output: 13

Test network against noisy input:
1.2	1.2	1.3	0.4	Output: 0
1.5	1.0	0.1	0.5	Output: 8
0.4	1.1	1.2	0.6	Output: 9
1.0	0.5	1.1	0.2	Output: 3
1.5	0.1	0.1	0.2	Output: 4
0.5	1.4	0.1	0.5	Output: 5
0.5	0.4	1.5	0.0	Output: 3
1.5	1.5	1.2	1.0	Output: 7
1.3	1.5	0.3	1.4	Output: 8
0.4	1.3	1.4	1.5	Output: 9
1.3	0.3	1.3	1.4	Output: 10
1.2	0.3	0.3	1.3	Output: 11
0.1	1.2	0.4	1.2	Output: 9
0.1	0.1	1.1	1.1	Output: 13