Perceptron Training

One way to learn the weights is to start with random weights then apply it to each example, changing the weights to fit.
The perceptron training rule is used to change the weights each time. It does $w_{i} \leftarrow w_{i} + Δ w_{i}$ where $Δ w_{i} = η (t - o) x_{i}$ and $t = c (\overset{⇀}{x})$ is target value, $o$ is perceptron output, and $η$ is small constant (e.g., .1) called learning rate.