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Perform two training steps for the network as shown in figure given using the perceptron learning rule. Assume the training data 2. Xj = 1 d1 =0 X2 = [2 d2 =1 .2. And the initial weight vector w¡ = with learning constant c = .5 ip1 W1 W 2 ip: f(net) net W 3 ip 3 AWi d; Learning Signal r X;

Perform two training steps for the network as shown in figure given using the perceptron learning rule. Assume the training data 2. Xj = 1 d1 =0 X2 = [2 d2 =1 .2. And the initial weight vector w¡ = with learning constant c = .5 ip1 W1 W 2 ip: f(net) net W 3 ip 3 AWi d; Learning Signal r X;

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Perform two training steps for the network as shown in figure given using the perceptron learning rule. Assume the training data 2. Xj = 1 d1 =0 X2 = [2 d2 =1 .2. And the initial weight vector w¡ = with learning constant c = .5
Transcribed Image Text:Perform two training steps for the network as shown in figure given using the perceptron learning rule. Assume the training data 2. Xj = 1 d1 =0 X2 = [2 d2 =1 .2. And the initial weight vector w¡ = with learning constant c = .5
ip1 W1 W 2 ip: f(net) net W 3 ip 3 AWi d; Learning Signal r X;
Transcribed Image Text:ip1 W1 W 2 ip: f(net) net W 3 ip 3 AWi d; Learning Signal r X;
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