# AND GATE Perceptron Training Rule Machine Learning

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### Truth Table of AND Logical GATE is,

Weights w1 = 1.2, w2 = 0.6, Threshold = 1 and Learning Rate n = 0.5 are given

For Training Instance 1: A=0, B=0 and Target = 0

wi.xi = 0*1.2 + 0*0.6 = 0

This is not greater than the threshold of 1, so the output = 0, Here the target is same as calculated output.

For Training Instance 2: A=0, B=1 and Target = 0

wi.xi = 0*1.2 + 1*0.6 = 0.6

This is not greater than the threshold of 1, so the output = 0. Here the target is same as calculated output.

For Training Instance 2: A=1, B=0 and Target = 0

wi.xi = 1*1.2 + 0*0.6 = 1.2

This is greater than the threshold of 1, so the output = 1. Here the target does not match with the calculated output.

Hence we need to update the weights.

Now,

After updating weights are w1 = 0.7, w2 = 0.6 Threshold = 1 and Learning Rate n = 0.5

w1 = 0.7, w2 = 0.6 Threshold = 1 and Learning Rate n = 0.5

For Training Instance 1: A=0, B=0 and Target = 0

wi.xi = 0*0.7 + 0*0.6 = 0

This is not greater than the threshold of 1, so the output = 0. Here the target is same as calculated output.

For Training Instance 2: A=0, B=1 and Target = 0

wi.xi = 0*0.7 + 1*0.6 = 0.6

This is not greater than the threshold of 1, so the output = 0. Here the target is same as calculated output.

For Training Instance 3: A=1, B=0 and Target = 0

wi.xi = 1*0.7 + 0*0.6 = 0.7

This is not greater than the threshold of 1, so the output = 0. Here the target is same as calculated output.

For Training Instance 4: A=1, B=1 and Target = 1

wi.xi = 1*0.7 + 1*0.6 = 1.3

This is greater than the threshold of 1, so the output = 1. Here the target is same as calculated output.

Hence the final weights are w1= 0.7 and w2 = 0.6, Threshold = 1 and Learning Rate n = 0.5.

## Summary

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