Suppose x, and x are two input neurons and z, and z2 are the hidden neurons...

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Suppose x, and x are two input neurons and z, and z2 are the hidden neurons with single output y in a neural network. Weights between input and hidden neurons are {v., Vas, Viz, Vaz), and between hidden ad output neurons are {ws, wa). The weights and input neurons are initialized in the following way (V1, V21, V12 V22)=(0.6, -0.1, -0.3, 0.4} {W, W}={0.4, 0.1} {X, X}={0,1} Learning rate a=0.5 Activation function (c) = Target t=1 Using a Back Propagation neural network (BPN), Calculate the error at the output layer 1+8-* Suppose x, and x are two input neurons and z, and z2 are the hidden neurons with single output y in a neural network. Weights between input and hidden neurons are {v., Vas, Viz, Vaz), and between hidden ad output neurons are {ws, wa). The weights and input neurons are initialized in the following way (V1, V21, V12 V22)=(0.6, -0.1, -0.3, 0.4} {W, W}={0.4, 0.1} {X, X}={0,1} Learning rate a=0.5 Activation function (c) = Target t=1 Using a Back Propagation neural network (BPN), Calculate the error at the output layer 1+8-*

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In the neural network described we have two input neurons x1 and x2 two hidden neurons z1 and z2 and one output neuron y The goal is to calculate the ... View the full answer