Pattern Recognition 3: Neural networks

Neural networking is a computational model on how neurons work. Like an actual brain, neural networks 'learn' the recognition rules used to perform an operation through examples. The larger the example size, the more accurate the operation. This technique is preferred than linear discriminant analysis due to having a faster processing speed after it has learned.

The basic mathematical construct of a neuron is shown below:

A neuron accepts weighted inputs and then sums them up. This sum is then acted on by an activation function g which outputs the new signal z.

By connecting many neurons together, we are able to create a neural network. A typical network consist of an input layer, hidden layer, and output layer:

By applying he neural network algorithm to my object classes, I was able to obtain a 37.5% recognition rate. the poor recognition rate may be due to the small sample training size used. Neural networks need a large training sample size in order to accurately process incoming information.

Since the code for neural network was already given to us and because I hardly understood what it does, I give myself a 6/10 for this activity.