A comparison of neural network models for pattern recognition

Abstract

A brief survey of the existing neural network models for signal/image processing and pattern recognition is presented. A comparison of the back-propagation algorithm for multilayer perception and an adaptive sample set construction procedure offered by Nestor's restricted Coulomb energy network is presented. A performance comparison with real data for ultrasonic nondestructive evaluation of materials is presented.