The input vector of the network is x = [X, X2, X3], the vector of hidden...

 

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The input vector of the network is x = [X₁, X2, X3], the vector of hidden layer output is y=[y1, y2], the output of the actual outputs is z= [z1, z2, z3]. The network has the following weight vector: Assume that all units have bipolar binary activation function in hidden layer, and all units have linear activation function. If the network is tested with an input vector x= [1.5, 2, 3.5]. Compute the output y2 and 23. 1.0 V1 0.4 -0.6 1.9 V2 -1.2 0.5 -0.7 W1 -3.5 W2 0.5 -1.2 0 W3 0 0.3 0.6 The input vector of the network is x = [X₁, X2, X3], the vector of hidden layer output is y=[y1, y2], the output of the actual outputs is z= [z1, z2, z3]. The network has the following weight vector: Assume that all units have bipolar binary activation function in hidden layer, and all units have linear activation function. If the network is tested with an input vector x= [1.5, 2, 3.5]. Compute the output y2 and 23. 1.0 V1 0.4 -0.6 1.9 V2 -1.2 0.5 -0.7 W1 -3.5 W2 0.5 -1.2 0 W3 0 0.3 0.6

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Solution To compute the output y2 and z3 we need to calculate the weigh... View the full answer