### OR GATE Perceptron Training Rule – Artificial Neural Network in Machine Learning – 17CS73

### Video Tutorial

### Truth Table of OR Logical GATE is,

Weights w1 = 0.6, 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*0.6 + 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 = 1

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

This is not greater than the threshold of 1, so the output = 0. Here the target does not match with calculated output.

Now,

Weights w1 = 0.6, w2 = 1.1, Threshold = 1 and Learning Rate n = 0.5 are given

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

wi.xi = 0*0.6 + 0*1.1 = 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 = 1

wi.xi = 0*0.6 + 1*1.1 = 1.1

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 = 1

wi.xi = 1*0.6 + 0*1.1 = 0.6

This is not greater than the threshold of 1, so the output = 0. Here the target does not match with calculated output.

Now,

Weights w1 = 1.1, w2 = 1.1, Threshold = 1 and Learning Rate n = 0.5 are given

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

wi.xi = 0*2.2 + 0*1.1 = 0

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

wi.xi = 0*1.1 + 1*1.1 = 1.1

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

wi.xi = 1*1.1 + 0*1.1 = 1.1

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

wi.xi = 1*1.1 + 1*1.1 = 2.2

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

**Final wieghts w1 = 1.1, w2 = 1.1 Threshold = 1 and Learning Rate n = 0.5**.

## Summary

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