Activity 15 - Neural Networks

A neural network is typically a computational model which mimics the processing model of the neurons in the human brain. The 'neurons' on said network has an advantage over Linear Discriminant Analysis in that it does not require a given boundaries or rules in order to perform the classification, it 'learns' these rules for itself through a given set of weighed inputs.

Fig. 1. Artificial Neural Network [1]

Consider the figure above, let us consider that the input is a vector and each given input node represents one dimension of the vector. Each input is given a given weight, and when processed gives out a variable a. This variable act on the activation function g, which gives the output z. This model describes the basic mechanics of a feedforward neural network. For this activity the Artificial Neural Network toolbox for Scilab was downloaded and the feature data from Activity 14 was used. 

A feedforward neural network was created, 3 feature data was used as input, run through one hidden layer, and the resulting classification would be represented in the form of three binary digits.

0 1 0 - green rectangle

1 0 0 - red rectangle

1 1 0 - yellow rectangle

0 1 1 - green circle

1 0 1 - red circle

The same training population was used and same number of instances were used for classification. A learning rate of 2.5 with the network trained for 400 repetitions for the weight. Using this process a 100% rate of classification was achieved when performed for 50 repetitions with random training population sampling.

I would like to give myself a grade of 10 for this activity for understanding and implementing the needed procedure.

Reference:

[1] M. Soriano, 'Neural Networks', Applied Physics 186 Activity Manual 2013