Implementation of XOR Gate Using Multi-Layer Perceptron/ Error Back Propagation

Pop Up Procedure

Simulation

→ Click on any line to change its weight

→ Click on the threshold graph to change threshold value

→ Click on any hidden/output neuron to change its bias

→ You cannot change the parameters once you've started simulations.

→ The red line in the decision boundaries graph depicts the boundary formed due to hidden neuron 1, blue line corresponds to hidden neuron 2, and green line to the output neuron respectively.


Select a network:


W11 0 W12 0 W21 0 W22 0 V1 0 V2 0 y Output Neuron Input 1 Input 2 Hidden Neuron 1 Hidden Neuron 2 b1 = 0 b2 = 0 b3 = 0 Threshold = 0

Truth Table


Input Output
X1 X2 Output of hidden neuron 1 Output of hidden neuron 2 Final Network Output Expected Output
0 0 - - - 0
0 1 - - - 1
1 0 - - - 1
1 1 - - - 0
Set Learning rate:  1



Set the no.of iterations between 1000 & 100000000





Slide to change weight

Slide to change threshold

Slide to change bias