What is the difference between a Perceptron, Adaline and a neural network model?

Both Adaline and Perceptron are neural network models (single layer).

The Perceptron is one of the oldest and most simple learning algorithms in existence, and would consider Adaline as an improvement over Perceptron.

The differences between Perceptron and Adaline

• The Perceptron uses the class labels to learn the coefficients of the model.
• Adaline uses continuous predictive values ​​(from the net input) to learn the coefficients of the model, which is more «powerful» because it tells us by «how much» we were good or bad.

Therefore, in the perceptron, as illustrated below, we simply use the predicted class labels to update the weights, and in Adaline, we use a continuous response: