Programming Language: MATLAB

3. This problem trains a simple artificial neural network (ANN) with a single neuron to divide the x-y plane into the classes of blue and yellow points shown below. These four points are saved in the file HW05data mat as the vectors x and y (although any four, or more!, points could be used) 0.8 0.6 0.4 0.B 0.5(x3) 0.6 0.4 0.2 0.2 0.5 0.5 Data with which to train the ANN Output of the trained ANN over the domain Output of the neural network is computed for any (r,y) point given the network parameters u, v, and b. It is desired that z be near zero in the vicinity of points 1 and 2 (blue) while producing near unity values for points 3 and 4 (yellow). Accuracy of the ANN is evaluated by the sum squared error 1+e which judges how close the ANN models the desired values for the four (x.y) pairs. Optimal values for network parameters u, v, and b are determined through gradient descent, which aims to update them depending on how 3. This problem trains a simple artificial neural network (ANN) with a single neuron to divide the x-y plane into the classes of blue and yellow points shown below. These four points are saved in the file HW05data mat as the vectors x and y (although any four, or more!, points could be used) 0.8 0.6 0.4 0.B 0.5(x3) 0.6 0.4 0.2 0.2 0.5 0.5 Data with which to train the ANN Output of the trained ANN over the domain Output of the neural network is computed for any (r,y) point given the network parameters u, v, and b. It is desired that z be near zero in the vicinity of points 1 and 2 (blue) while producing near unity values for points 3 and 4 (yellow). Accuracy of the ANN is evaluated by the sum squared error 1+e which judges how close the ANN models the desired values for the four (x.y) pairs. Optimal values for network parameters u, v, and b are determined through gradient descent, which aims to update them depending on how