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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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