Consider a Perceptron with 3 inputs, x1, x2, x3, and one output unit that uses the...

   

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Consider a Perceptron with 3 inputs, x1, x2, x3, and one output unit that uses the Signum function. sgn(x)=. J+1 if x0 (-1 if x <0 Assume initial weights w1 = 0.7, w2 = 0.9, w3 = 0.2, learning rate 0.3, and bias w4=-0.5, Hence we (the desired output should be less than 0) WA W W :have a) What is the output of the Perceptron given the inputs x1=1, x2=0, x3=1 x4=1? Show your work. b) What are the updated weights' values after applying the Perceptron Learning Rule with the above input and teacher output 0? (means: for the output 0 the desired output function is less than 0 Consider a Perceptron with 3 inputs, x1, x2, x3, and one output unit that uses the Signum function. sgn(x)=. J+1 if x0 (-1 if x <0 Assume initial weights w1 = 0.7, w2 = 0.9, w3 = 0.2, learning rate 0.3, and bias w4=-0.5, Hence we (the desired output should be less than 0) WA W W :have a) What is the output of the Perceptron given the inputs x1=1, x2=0, x3=1 x4=1? Show your work. b) What are the updated weights' values after applying the Perceptron Learning Rule with the above input and teacher output 0? (means: for the output 0 the desired output function is less than 0