2) a) Write the algorithm (in steps)used in Self Organizing Feature Map (SOFM) to update the weights of the Feature map. 3 3 b) Our aim is to classify the three classes , namely C = 29 2 3 4 4 2 2 2. 4 C 20 and C= 3 5 from each other. -1 3 To achieve this goal, we are going to use a self organizing lattice network structure that is represented in two dimensional (in 3x3 matrix) form. Assume that the initial centers are given as follows 0.2 0.1 (-0.2 0.4 -0.1 -0.1 (-0.2 C = 0.1 0 0.4 -0.2 0 0.8 0.1 0.2 0.5 0.1 0.6 0.3 -0.1 0 -0.4 0.6 0.1 0 0.7 -0.7 and also assume that the learning parameter n=0.9 and the neighborhood function A=l , update the weights (using SOFM algorithm) for one iteration using the first element of each class as a training data(as given below); (2 3 CC) 2) a) Write the algorithm (in steps)used in Self Organizing Feature Map (SOFM) to update the weights of the Feature map. 3 3 b) Our aim is to classify the three classes , namely C = 29 2 3 4 4 2 2 2. 4 C 20 and C= 3 5 from each other. -1 3 To achieve this goal, we are going to use a self organizing lattice network structure that is represented in two dimensional (in 3x3 matrix) form. Assume that the initial centers are given as follows 0.2 0.1 (-0.2 0.4 -0.1 -0.1 (-0.2 C = 0.1 0 0.4 -0.2 0 0.8 0.1 0.2 0.5 0.1 0.6 0.3 -0.1 0 -0.4 0.6 0.1 0 0.7 -0.7 and also assume that the learning parameter n=0.9 and the neighborhood function A=l , update the weights (using SOFM algorithm) for one iteration using the first element of each class as a training data(as given below); (2 3 CC)