Improving discriminant neural network (DNN) design by the use of principal component analysis

Investigations of the design of a discriminant neural network (DNN) have shown the advantages of sequential design of hidden nodes and pruning of the training data for improved classification and fast training time. The performance can be further improved by adding the capability of a nonlinear, principal component discriminant node. This type of hidden node is useful for separating classes which have common mean vectors and are overlapped on one other.