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Conisder the following single layer trained neural network. Input nodes X₁- X₂- Y = f(Σ,w₁X₁ +t>0) where f(z)={ C. ifz is true C otherwise 0 1 1 0 0 1 0 Assigne class labels to the following test data set: X₁ X2 X3 Y 0 0 0 0 0 1 1 1 1 1 0 1 1 1 - Black box W₁=0.2 W₂=0.2 W₂= -0.5 Σ|- t=0.2 Output node Y

Conisder the following single layer trained neural network. Input nodes X₁- X₂- Y = f(Σ,w₁X₁ +t>0) where f(z)={ C. ifz is true C otherwise 0 1 1 0 0 1 0 Assigne class labels to the following test data set: X₁ X2 X3 Y 0 0 0 0 0 1 1 1 1 1 0 1 1 1 - Black box W₁=0.2 W₂=0.2 W₂= -0.5 Σ|- t=0.2 Output node Y

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Conisder the following single layer trained neural network. Y=f(Σ,w,X, +t> 0) where f(2)= 1 0 1 1 Input nodes [C, ifz is true C otherwise Assigne class labels to the following test data set: X₁ X2 X3 Y 0 0 0 0 1 1 0 0 1 1 1 0 1 1 1 0 0 X₁ X₂- X3- -- Black box -- W₁=0.2 W₂=0.2 Output node EI Y W₁=-0.5 t=0.2
Transcribed Image Text:Conisder the following single layer trained neural network. Y=f(Σ,w,X, +t> 0) where f(2)= 1 0 1 1 Input nodes [C, ifz is true C otherwise Assigne class labels to the following test data set: X₁ X2 X3 Y 0 0 0 0 1 1 0 0 1 1 1 0 1 1 1 0 0 X₁ X₂- X3- -- Black box -- W₁=0.2 W₂=0.2 Output node EI Y W₁=-0.5 t=0.2
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