Neural network computing system for pattern recognition of therm

Radiant energy – Invisible radiant energy responsive electric signalling – Infrared responsive

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2503416, 395 23, G01J 3433

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056314698

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 implemented with a network of known microprocessor chips.

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A.H. Carrieri, "Infrared Detection of Liquids on Terrestrial Surfaces by sub.2 Laser Heating," Appl. Opt., 29(33), 4907-4914, 1990 no date.
H.F. Hameka and J.O. Jensen, "Theoretical Prediciton of the Infrared and Raman Spectra of O-ethyl S-2diisopropyl amino ethyl eethyl phosphonothiolate," Int. J. Quant. Chem., (vol. 50), 161-172, 1994 no month.
A.H. Carrieri and Pascal I.Lim, "Neural Network Pattern Recognition of Thermal-Signature Spectra for Chemical Defense" Appl. Optics, vol.34 No. 15 2623-2635 (1995) no month.

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