Activity 16 Neural Networks

In this activity, pattern recognition is employed by using neural networks...

Neural network is a computational model that mimics the behavior of the neurons in the brain [1]. It is more advantageous than linear discriminant analysis because it also considers nonlinear systems. It is able to model nonlinear functions containing large variables which make it useful to several applications such as detection of medical phenomena, stock market prediction, credit assignment, monitoring the condition of machinery, and engine machinery [2].

To implement neural network model, I considered 10 sample objects consisting of blue and red dinosaurs as shown in Figure 1. Four feature vectors, namely the red chromaticity value, green chromaticity value, blue chromaticity value, and area were utilized for classification. The area values of the objects were normalized with respect to the highest value so the feature vectors can be correlated with one another. The first five rows of each column correspond to the blue dinosaurs while the last five rows describe the feature vectors of the red dinosaurs. The matrix below shows the arrangement of the training set and the feature vectors. The first, second, third, and fourth columns are designated to red chromaticity value, green chromaticity value, blue chromaticity value, and normalized area, respectively.

Figure 1. Image used for neural network

For this case, I chose the red dinosaurs as my desired class, so the desired output is written such that the class of blue dinosaurs is set to 0 and red dinosaurs to 1. By adopting the code of Mr. Jeric Tugaff for neural network posted in Mr. Cole’s blog [3], the results shown in Figure 2 and 3 were obtained. The number of iterations (T) and learning rate (LR) were varied and their effects to the accuracy of recognition were observed. The values for T were changed from 100 to 500 with an increment of 100, and it was found that more accurate pattern recognition is acquired for higher T, that is, blue-colored dinosaurs resulted to values nearest to 0 and red dinosaurs nearest to 1. Thus, T=500 yield the most accurate pattern recognition ability from the range chosen, and it was utilized for determining the effect of LR to the accuracy of recognition (see Figure 3). LR was varied from 0.1 to 2 and it can be observed that more accurate pattern recognition is attained as the LR is increased. From the range of LR values, LR=2 yielded the highest accuracy for pattern recognition by having values of red dinosaurs closest to 1 and blue dinosaurs closest to 0. However, it cannot be concluded that T=500 and LR=2 are the optimized parameters for acquiring the most accurate pattern recognition ability since we can still extend the range and seek for more combination of LR and T.

Figure 2. Results obtained for different T


Figure 3. Results obtained for different LR

Since I was able to meet the objective of the activity, I give myself a grade of 10. I would like to acknowledge Master and Orly for helping me improve my results.

References:

1. "Neural Networks", Activity 16 Manual.

2. "Neural Networks", Statsoft Inc., http://www.statsoft.com/textbook/stneunet.html#apps.

3. http://cole-ap186.blogspot.com/