This paper describes the development of the utility of a dynamic neural network known as projection network for pattern classification. It first gives the derivation of the projection network, and then describes the network architecture and analyzes properties such as equilibrium points and their stability condition. The procedures for utilizing the projection network for pattern classification problem are established and the benefits are discussed. The proposed classification system is then tested with well-known benchmark data sets, namely the Fisher’s iris data, the heart disease data and the credit screening data and the results are compared to other classifiers including Neural Network Rule Base (NNRB), Genetic Algorithm Rule Base (GARB), Rough Set, and C4.5 decision tree.
Optimization in an Error Backpropagation Neural Network Environment with a Performance Test on a Spectral Pattern Classification Problem
