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Perceptron Learning Example
- The applied algorithm is as follows
- Initialize the weights and threshold to small random numbers.
- Present a vector x to the neuron inputs and calculate the output.
- Update the weights according to the error.
- Applied learning function: Wj(t+1) ← Wj(t) + α × (y-gw(x)) × xj
- Example with two inputs x1, x2
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