A single-neuron network using f(net) = sgn (net) as in Figure (3.2) has been trained using...

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A single-neuron network using f(net) = sgn (net) as in Figure (3.2) has been trained using the pairs of x₁, d; as shown below: 3 d₁ = -1 ---·)· (~- -[-] · -~-~). dz = 1 X3 = d3 -1 The final weights obtained using the perceptron rule are w = [3 2 6 1]' Knowing that correction has been performed in each step for c = 1, determine the following weights: (a) w³, w², w¹ by back-tracking the training (b) w5, w, w7 obtained for steps 4, 5, and 6 of training by reusing the sequence (x₁, d₁), (x₂, d₂), (X3, d3). A single-neuron network using f(net) = sgn (net) as in Figure (3.2) has been trained using the pairs of x₁, d; as shown below: 3 d₁ = -1 ---·)· (~- -[-] · -~-~). dz = 1 X3 = d3 -1 The final weights obtained using the perceptron rule are w = [3 2 6 1]' Knowing that correction has been performed in each step for c = 1, determine the following weights: (a) w³, w², w¹ by back-tracking the training (b) w5, w, w7 obtained for steps 4, 5, and 6 of training by reusing the sequence (x₁, d₁), (x₂, d₂), (X3, d3).

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Answer x1 x2 x3 x4 d net fnet do w1 1 w2 0 w... View the full answer