Accession Number : ADP007151
Title : Simulation Experiments for Neural Network Learning,
Corporate Author : BOEING COMPUTER SERVICES CO SEATTLE WA
Personal Author(s) : Newman, David S.
Report Date : 1992
Pagination or Media Count : 4
Abstract : This paper investigates approaches to the design of simulation experiments for training neural networks which are to be used as classifiers. Hierarchical clustering applied to the ART1 and ART2 (ART = Adaptive Resonance Theory) neural network architectures developed by Carpenter and Grossberg 20,21) is the basis for the approach. A series of experiments based on this approach will test the performance of ART1 and ART2 as pattern classifiers against a variety of real and artificial data sets. The issues to be investigated in these experiments include the sensitivity of performance to a variety of network parameters, pattern characteristics, and pattern presentation disciplines. A background is provided for those unfamiliar with neural networks in general, and with Grossberg's approach in particular.
Descriptors : *CLUSTERING, *NEURAL NETS, APPROACH, BACKGROUND, NETWORKS, PAPER, PARAMETERS, PATTERNS, RESONANCE, SENSITIVITY, SIMULATION, TEST AND EVALUATION, TRAINING.
Subject Categories : Biology
Distribution Statement : APPROVED FOR PUBLIC RELEASE