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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