Nural network

E7.7 Consider the reference patterns and targets given below. We want to use these data to train a linear associator network. {P. - 44[2]} {0:- ) --- []} {s: - -s = [-2]} i. Use the Hebb rule to find the weights of the network. ii. Find and sketch the decision boundary for the network with the Hebb rule weights. iii. Use the pseudo-inverse rule to find the weights of the network. Be. cause the number, R, of rows of Pis less than the number of col- umns, Q, of P, the pseudoinverse can be computed by p* -p'(PP'). iv. Find and sketch the decision boundary for the network with the pseudo-inverse rule weights. v. Compare discuss) the decision boundaries and weights for each of the methods (Hebb and pseudo-inverse). E7.7 Consider the reference patterns and targets given below. We want to use these data to train a linear associator network. {P. - 44[2]} {0:- ) --- []} {s: - -s = [-2]} i. Use the Hebb rule to find the weights of the network. ii. Find and sketch the decision boundary for the network with the Hebb rule weights. iii. Use the pseudo-inverse rule to find the weights of the network. Be. cause the number, R, of rows of Pis less than the number of col- umns, Q, of P, the pseudoinverse can be computed by p* -p'(PP'). iv. Find and sketch the decision boundary for the network with the pseudo-inverse rule weights. v. Compare discuss) the decision boundaries and weights for each of the methods (Hebb and pseudo-inverse)