Sigmoid Unit

Artificial Neural Networks

We let $σ (net)$ be the sigmoid function where $σ (net) = \frac{1}{1 + e^{- net}}$ and $net$ is the sum of the weighted inputs, or $net = \sum_{i = 1}^{n} w_{i} x_{i}$
It has the nice property that $\frac{d σ (x)}{dx} = σ (x) (1 - σ (x))$ .
So, we can use it to derive gradient descent rules for the training of multilayer networks.