Needs to be done in 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 HWidata.mat as the vectors x and y (although any four, or more!, points could be uscd). (x,,ys) 0.6 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 xy point given the network parameters u, v, and b. It is desired that z be near zero in the vicinity of points 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 (xy) pairs. Optimal values for network parameters . 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 HWidata.mat as the vectors x and y (although any four, or more!, points could be uscd). (x,,ys) 0.6 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 xy point given the network parameters u, v, and b. It is desired that z be near zero in the vicinity of points 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 (xy) pairs. Optimal values for network parameters . v, and b are determined through gradient descent, which aims to update them depending on how