Neural network computing system for pattern recognition of thermoluminescence signature spectra and chemical defense
First Claim
1. A system for recognizing compositions of matter in accordance with thermoluminescence exhibited by the compositions of matter, the system comprising:
- sensor means for detecting the thermoluminescence;
spectral analysis means for analyzing the thermoluminscence detected by the sensor means to produce spectral data for a plurality of wave numbers; and
artificial neural network means for receiving the spectral data and for determining, in accordance with the spectral data, whether the thermoluminescence detected by the sensor means indicates the presence of the compositions of matter.
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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≦ν≦1400 wavenumbers with resolution Δν=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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Citations
19 Claims
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1. A system for recognizing compositions of matter in accordance with thermoluminescence exhibited by the compositions of matter, the system comprising:
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sensor means for detecting the thermoluminescence;
spectral analysis means for analyzing the thermoluminscence detected by the sensor means to produce spectral data for a plurality of wave numbers; andartificial neural network means for receiving the spectral data and for determining, in accordance with the spectral data, whether the thermoluminescence detected by the sensor means indicates the presence of the compositions of matter. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method of training an artificial neural network to recognize compositions of matter in accordance with thermoluminescence exhibited by the compositions of matter, the method comprising:
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(a) providing known absorption spectra for the compositions of matter; (b) differentiating the known absorption spectra with respect to absorption time to derive differentiated absorption spectra; and (c) training the artificial neural network by backward error propagation with the known absorption spectra and the differentiated absorption spectra to obtain a plurality of neural network weights for the artificial neural network. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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Specification