Weight configurations of trained perceptrons.

We strive to predict the function mapping and rules performed by a trained perceptron from studying the weights. We derive a few properties of the trained weights and show how the perceptron's representation of knowledge, rules and functions depend on these properties. Two types of perceptrons are studied--one case with continuous inputs and one hidden layer, the other a simple binary classifier with boolean inputs and no hidden units.