ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer neural network and the name of the physical device that implemented this network. It was developed by Professor Bernard Widrow and his graduate student Ted Hoff at Stanford University in 1960. It is based on the McCulloch–Pitts neuron. It consists of a weight, a bias and a summation function.
The difference between Adaline and the standard (McCulloch–Pitts) perceptron is that in the learning phase the weights are adjusted according to the weighted sum of the inputs (the net). In the standard perceptron, the net is passed to the activation (transfer) function and the function's output is used for adjusting the weights.
There also exists an extension known as Madaline.
Adaline is a single layer neural network with multiple nodes where each node accepts multiple inputs and generates one output. Given the following variables:
- x is the input vector
- w is the weight vector
- n is the number of inputs
- some constant
- y is the output
then we find that the output is . If we further assume that
then the o/p reduces to the dot product of x and w
Let us assume:
- is the learning rate (some constant)
- is the desired output
- is the actual output
- "Delta Learning Rule: ADALINE". Artificial Neural Networks. Universidad Politécnica de Madrid.