Activity 16: Neural Networks

For this activity, we again classify objects but this time using neural networks. A neural network consists of three parts - the input layer, hidden layer, and the output layer. For our purposes, the input layer consists of the features of the test objects, hidden layer consists of the features of the training objects, and the output layer is the classification of the input layer after it is compared with the hidden layer. In Scilab,to do the classification by neural networks, we need to load the ANN_toolbox and a code courtesy of Jeric Tugaff. The same features from the two previous activities were used in order to compare the efficiency of the techniques we have learned so far. It is imporatant to note that the features should be normalized to be able to classify objects using this method. After implementing the code to the test objects, the output are as follows:

After rounding off the output of the program, we can see that we got a perfect classification. ^_^

For this activity, I give myself a 10/10. Thank you to Gilbert for helping me in this activity.

References:
Maricor Soriano, PhD. Activity 16 - Neural Networks. AP 186 manual.