Artificial Neuron - Spreadsheet Example

Spreadsheet Example

Input Initial Output Final
Threshold Learning Rate Sensor values Desired output Weights Calculated Sum Network Error Correction Weights
TH LR X1 X2 Z w1 w2 C1 C2 S N E R W1 W2
X1 x w1 X2 x w2 C1+C2 IF(S>TH,1,0) Z-N LR x E R+w1 R+w2
0.5 0.2 0 0 0 0.1 0.3 0 0 0 0 0 0 0.1 0.3
0.5 0.2 0 1 1 0.1 0.3 0 0.3 0.3 0 1 0.2 0.3 0.5
0.5 0.2 1 0 1 0.3 0.5 0.3 0 0.3 0 1 0.2 0.5 0.7
0.5 0.2 1 1 1 0.5 0.7 0.5 0.7 1.2 1 0 0 0.5 0.7
0.5 0.2 0 0 0 0.5 0.7 0 0 0 0 0 0 0.5 0.7
0.5 0.2 0 1 1 0.5 0.7 0 0.7 0.7 1 0 0 0.5 0.7
0.5 0.2 1 0 1 0.5 0.7 0.5 0 0.5 0 1 0.2 0.7 0.9
0.5 0.2 1 1 1 0.7 0.9 0.7 0.9 1.6 1 0 0 0.7 0.9
0.5 0.2 0 0 0 0.7 0.9 0 0 0 0 0 0 0.7 0.9
0.5 0.2 0 1 1 0.7 0.9 0 0.9 0.9 1 0 0 0.7 0.9
0.5 0.2 1 0 1 0.7 0.9 0.7 0 0.7 1 0 0 0.7 0.9
0.5 0.2 1 1 1 0.7 0.9 0.7 0.9 1.6 1 0 0 0.7 0.9

Supervised neural network training for an OR gate.

Note: Initial weight equals final weight of previous iteration.

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