title: Neural network pattern recognition by means of differential absorption Mueller matrix spectroscopy.
abstract: Artificial neural network systems were built for detecting amino acids, sugars, and other solid organic matter by pattern recognition of their polarized light scattering signatures in the form of a Mueller matrix. Backward-error propagation and adaptive gradient descent methods perform network training. The product of the training is a weight matrix that, when applied as a filter, discerns the presence of the analytes on the basis of their cued susceptive Mueller matrix difference elements. This filter function can be implemented as a software or a hardware module to a future differential absorption Mueller matrix spectrometer.
authors.affiliation: US Army Soldier Biological Chemical Command, Edgewood Chemical and Biological Center, Research and Technology Directorate, Aberdeen Proving Ground, Maryland 21010-5424, USA. Arthur.Carrieri@SBCCOM.apgea.army.mil
authors.forename: A H
authors.initials: AH
authors.lastname: Carrieri