Accession Number : ADA231584
Title : Computing with Neural Networks: A Comparison of the Multi-Layer Perceptron and the Hierarchical Pattern Recognition Network for Classification Problems.
Descriptive Note : Master's thesis,
Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH
Personal Author(s) : Christian, Stacey W.
Report Date : 1990
Pagination or Media Count : 104
Abstract : Two artificial neural networks are compared in the realm of pattern recognition problems: the Multi-Layer Perception (MLP) using the Back Propagation algorithm for training and the Hierarchical Pattern Recognition Network (HPR). Both systems' architecture, node characteristics, and training algorithms are fully developed. Their decision boundary regions are demonstrated graphically on a series of 2 dimensional 2-class problems. The performance of both networks is tested on the Oxford Alvey Vowel Database and LANL's Stamped Metal Database. Additionally, LeCun's theory of Optimal Brain Damage is applied to the MLP in an attempt to reduce network size. The two case studies demonstrated that both networks perform equally well on real-world applications. The HPR network however, proved to be more advantageous in the realm of practical issues such as processing time, adaptability, memory requirements, and the ability to properly identify non-class data. LeCun's method of Optimal Brain Damage was effective in reducing the network size of the MLP and thus decreasing processing time as well as improving generalization performance.
Descriptors : ALGORITHMS, ARCHITECTURE, BLACK(COLOR), BOUNDARIES, BRAIN DAMAGE, CASE STUDIES, CLASSIFICATION, COMPARISON, DATA BASES, DECISION MAKING, HIERARCHIES, LAYERS, MEMORY DEVICES, METALS, NETWORKS, NEURAL NETS, NODES, OPTIMIZATION, PATTERN RECOGNITION, PERCEPTION, PROCESSING, PROPAGATION, REGIONS, REQUIREMENTS, SIZES(DIMENSIONS), TIME, TRAINING, VOWELS.
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE