Neural network computing system for pattern recognition of thermoluminescence signature spectra and chemical defense

US5631469A · kind A · utility

Assignee

• The United States of America as represented by the Secretary of the Army · US

Inventors

• Arthur H. Carrieri · Edgewood, US
• Pascal Lim · Baltimore, US

Key dates

• Filing date: Apr 15, 1996
• Grant date: May 20, 1997
• Expiry date: Apr 15, 2016

Classification

• Technology area (CPC G): Physics
• CPC primary: G06V30/194
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

A four-layer neural network is trained with data of midinfrared absorption by nerve and blister agent compounds (and simulants of this chemical group) in a standoff detection application. Known infrared absorption spectra by these analyte compounds and their computed first derivative are scaled and then transformed into binary or decimal arrays for network training by a backward-error-propagation (BEP) algorithm with gradient descent paradigm. The neural network transfer function gain and learning rate are adjusted on occasion per training session so that a global minimum in final epoch convergence is attained. Three successful neural network filters have been built around an architecture design containing: (1) an input layer of 350 neurons, one neuron per absorption intensity spanning 700.ltoreq..nu..ltoreq.1400 wavenumbers with resolution .DELTA..nu.=2; (2) two hidden layers in 256- and 128-neuron groups, respectively, providing good training convergence and adaptable for downloading to a configured group of neural IC chips; and (3) an output layer of one neuron per analyte--each analyte defined by a singular vector in the training data set. Such a neural network is preferably im…