Derivation of the multilayer perceptron weight constraints for direct network interpretation and knowledge discovery

This paper examines the multilayer perceptron (MLP) network from a hidden layer decision region perspective and derives the output layer and hidden layer weight constraints that the network must satisfy in performing a general classification task. This provides a foundation for direct knowledge discovery from the MLP, using a new method published by the author, which finds the key inputs that the MLP uses to classify an input case. The knowledge that the MLP network learns from the training examples is represented as ranked data relationships and induced rules, which can be used to validate the MLP network. The bounds of the network knowledge are established in the n-dimensional input space and a measure of the limit of the MLP network knowledge is proposed. An algorithm is presented for the calculation of the maximum number of hidden layer decision regions in the MLP input space.