Neural Network Task Four

The fourth task will involve using a Hopfeild network to recognize digit characters. I have created a training file containing only two digit characters, 7 and 8, from the data file IDEALTRN.NNA. These two characters were chosen because they have quite a great hamming distance. With a great hamming distance between stored patterns, the possibility of the network to perform well would be high. I have also created a testing file containing five example of each character 7 and 8 from the data file IDEALTST.NNA. A Hopfield network was then created using 48 input neurons, 48 hidden neurons and 48 output neurons, as there are 48 bits in each medium resolution character. The input and output layers act as a buffer and only the hidden layer do the processing. The network was trained and recalled one complete passes through the file. The output values are printed in the result file and these were compared with the input values contain in the training file to see how many correct outputs. All results were recorded.

I have done the same experiment as above using another pair of digit characters, 1 and 3, with a smaller hamming distance. It was found that it gives a poor results.