AND GATE Perceptron Training Rule – Artificial Neural Network in Machine Learning – 17CS73
Video Tutorial
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
This tutorial discusses the AND GATE Perceptron Training Rule in Machine Learning. If you like the tutorial share it with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.