Date of Award
12-1996
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Electrical and Computer Engineering
First Advisor
Steven K. Rogers, PhD
Abstract
This research develops a general methodology for designing neural network classifiers for real-world environmental problems. This methodology is demonstrated through the design of a multi-layer perceptron to classify stainless steel and actinide samples. Neural networks, sometimes called artificial neural networks, have been shown capable of classifying complex patterns. Artificial neural networks are physiologically motivated computer algorithms which attempt to mimic the function of the large interconnected network of neurons in the human brain, which has extraordinary pattern recognition capabilities. These artificial neural networks learn to map a set of input features, elemental composition, onto a set of outputs such as a binary node whose output (1 or 0) represents steel or not steel. For this reason, neural networks may be used to classify the given environmental data.
AFIT Designator
AFIT-GEE-ENG-96D-04
DTIC Accession Number
ADA321663
Recommended Citation
Blackmon, Jeffrey L., "Neural Network Classification of Environmental Samples" (1996). Theses and Dissertations. 5892.
https://scholar.afit.edu/etd/5892