The authors are with the U.S. Army Chemical and Biological Defense Command, Edgewood Research, Development, and Engineering Center, Aberdeen Proving Ground, Maryland 21010-5423. USA
Arthur H. Carrieri and Pascal I. Lim, "Neural network pattern recognition of thermal-signature spectra for chemical defense," Appl. Opt. 34, 2623-2635 (1995)
We treat infrared patterns of absorption or emission by nerve and blister agent compounds (and simulants of this chemical group) as features for the training of neural networks to detect the compounds’ liquid layers on the ground or their vapor plumes during evaporation by external heating. Training of a four-layer network architecture is composed of a backward-error-propagation algorithm and a gradient-descent paradigm. We conduct testing by feed-forwarding preprocessed spectra through the network in a scaled format consistent with the structure of the training-data-set representation. The best-performance weight matrix (spectral filter) evolved from final network training and testing with software simulation trials is electronically transferred to a set of eight artificial intelligence integrated circuits (ICs’) in specific modular form (splitting of weight matrices). This form makes full use of all input–output IC nodes. This neural network computer serves an important real-time detection function when it is integrated into pre- and postprocessing data-handling units of a tactical prototype thermoluminescence sensor now under development at the Edgewood Research, Development, and Engineering Center.
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Pattern-Matching Results for a Four-Layer, BEP Neural Network Fully Trained with Binary Spectral Representations of Nine Analyte Compoundsa
Binary Data-Set Representation
Correct Identification Agent (% False)
Incorrect Identification Agent (% True)
Training Spectrum
Testing Spectrum
Noise Added (%)
A0B, A1B, A2B
15
0 (98.7), 1 (98.7), 2 (98.7)
None
A3B, A4B, A5B
3 (98.7), 4 (98.7), 5 (98.7)
None
A6B, A7B, A8B
6 (98.7), 7 (98.7), 8 (98.7)
None
A0B, A1B, A2B
20
0 (98.7), 1 (98.7), 2 (98.7)
None
A3B, A4B, A5B
3 (98.7), 4 (98.7), 5 (98.7)
None
A6B, A7B, A8B
6 (98.7), 7 (98.7), 8 (98.7)
8 → 5 (98.4)
T0B
0
No ID
None
T3B
No ID
None
T4B
No ID
None
T5B
No ID
6 (98.4)
T6B
6 (90.0)
2 (98.7)
S2B
0
2 (98.7)
None
S3B
3 (98.7)
None
S8B
8 (98.7)
None
Input-layer neurons 350; first-hidden-layer neurons 256 and second-hidden-layer neurons 128; output-layer neurons 9; epoch error < 10−15; AB binary IR absorption spectrum (Nicolet 10-DX spectrometer measurement); i = (0, 1, 2, 3, 4, 5, 6, 7, 8) ≡ (VX, Vx, DIMP, DMMP, GA, GB, GD, GF, SF96); TB binary Gaussian 90 model theoretical prediction spectrum; SB binary interferometer sensor spectrum, final transfer function learning rate/gain 0.10/2.00; sigmoid limits ±1; and weight filter ±2.5. The binary divider is the intensity of the fourth-strongest peak per scaled absorption spectrum.
Table 3
Pattern-Matching Results for a Four-Layer, BEP Neural Network Fully Trained with (Left) Decimal and (Right) Derivative-Decimal Spectral Representations of Nine Analyte Compoundsa
Decimal-Data-Set Representation
Derivative-Decimal-Data Set Representation
Training Spectrum
Testing Spectrum
Added Noise (%)
Correct ID > 90% Agent (% True)
Incorrect ID > 90% Agent (% False)
Training Spectrum
Testing Spectrum
Added Noise (%)
Correct ID > 90% Agent (% True)
Incorrect ID > 90% Agent (% False)
A0D, A1D, A2D
25
0 (98.7), 1 (98.7), 2 (98.7)
None
13
0 (98.7), 1 (98.7), 2 (98.7)
None
A3D, A4D, A5D
3 (98.7), 4 (98.7), 5 (98.7)
None
3 (98.7), 4 (92.0), 5 (98.7)
None
A6D, A7D, A8D
6 (98.7), 7 (98.7), 8 (98.7)
None
6 (98.7), 7 (98.7), 8 (98.7)
None
A0D, A1D, A2D
27
0 (98.7), 1 (98.7), 2 (98.7)
None
19
0 (98.7), 1 (98.7), 2 (98.7)
None
A3D, A4D, A5D
3 (98.7), 4 (98.7), 5 (98.7)
None
3 (98.7), 4 (No ID), 5 (98.7)
None
A6D, A7D, A8D
6 (98.7), 7 (98.7), 8 (98.7)
6 → 7 (90.3)
6 (98.7), 7 (98.7), 8 (98.7)
None
T0D
0
No ID
7 (98.7)
0
No ID
3 (98.7), 5 (98.7), 6 (98.7)
T3D
3 (98.7)
None
No ID
1 (97.5), 5 (98.7), 8 (98.6)
T4D
4 (98.7)
1 (98.7), 3 (98.7), 6 (92.9), 7 (98.4)
No ID
None
T5D
No ID
2 (91.4), 7 (98.7)
No ID
None
T6D
6 (98.7)
2 (98.7), 7 (98.7)
No ID
None
S2D
0
2 (98.7)
None
0
—
—
S3D
3 (98.7)
None
—
—
S8D
8 (98.7)
None
—
—
Inupt layer neurons 350; first-hidden-layer neurons 256 and second-hidden-layer neurons 128; output-layer neurons 9; epoch error < 10−15; weight limits ±2.5; AD decimal IR absorption spectrum (Nicolet MX-1 or 10-DX spectrometer measurement);
decimal-first-derivitive absorption spectrum; i = (0, 1, 2, 3, 4, 5, 6, 7, 8) ≡ (VX, Vx, DIMP, DMMP, GA, GB, GD, GF, SF96); TD decimal Gaussian 90 model theoretical prediction spectrum;
decimal-first-derivitive theoretical prediction absorption spectrum; SD decimal interferometer sensor spectrum;
decimal-first-derivitive interferometer sensor spectrum. Left, transfer function gain 1.50, learning rate 0.120; right, transfer function gain 1.00, learning rate 0.200.
Tables (3)
Table 1
Chemical Agents and Simulants of Agents with Molecular Vibrational Modes Determined from Experimental Measurement
Pattern-Matching Results for a Four-Layer, BEP Neural Network Fully Trained with Binary Spectral Representations of Nine Analyte Compoundsa
Binary Data-Set Representation
Correct Identification Agent (% False)
Incorrect Identification Agent (% True)
Training Spectrum
Testing Spectrum
Noise Added (%)
A0B, A1B, A2B
15
0 (98.7), 1 (98.7), 2 (98.7)
None
A3B, A4B, A5B
3 (98.7), 4 (98.7), 5 (98.7)
None
A6B, A7B, A8B
6 (98.7), 7 (98.7), 8 (98.7)
None
A0B, A1B, A2B
20
0 (98.7), 1 (98.7), 2 (98.7)
None
A3B, A4B, A5B
3 (98.7), 4 (98.7), 5 (98.7)
None
A6B, A7B, A8B
6 (98.7), 7 (98.7), 8 (98.7)
8 → 5 (98.4)
T0B
0
No ID
None
T3B
No ID
None
T4B
No ID
None
T5B
No ID
6 (98.4)
T6B
6 (90.0)
2 (98.7)
S2B
0
2 (98.7)
None
S3B
3 (98.7)
None
S8B
8 (98.7)
None
Input-layer neurons 350; first-hidden-layer neurons 256 and second-hidden-layer neurons 128; output-layer neurons 9; epoch error < 10−15; AB binary IR absorption spectrum (Nicolet 10-DX spectrometer measurement); i = (0, 1, 2, 3, 4, 5, 6, 7, 8) ≡ (VX, Vx, DIMP, DMMP, GA, GB, GD, GF, SF96); TB binary Gaussian 90 model theoretical prediction spectrum; SB binary interferometer sensor spectrum, final transfer function learning rate/gain 0.10/2.00; sigmoid limits ±1; and weight filter ±2.5. The binary divider is the intensity of the fourth-strongest peak per scaled absorption spectrum.
Table 3
Pattern-Matching Results for a Four-Layer, BEP Neural Network Fully Trained with (Left) Decimal and (Right) Derivative-Decimal Spectral Representations of Nine Analyte Compoundsa
Decimal-Data-Set Representation
Derivative-Decimal-Data Set Representation
Training Spectrum
Testing Spectrum
Added Noise (%)
Correct ID > 90% Agent (% True)
Incorrect ID > 90% Agent (% False)
Training Spectrum
Testing Spectrum
Added Noise (%)
Correct ID > 90% Agent (% True)
Incorrect ID > 90% Agent (% False)
A0D, A1D, A2D
25
0 (98.7), 1 (98.7), 2 (98.7)
None
13
0 (98.7), 1 (98.7), 2 (98.7)
None
A3D, A4D, A5D
3 (98.7), 4 (98.7), 5 (98.7)
None
3 (98.7), 4 (92.0), 5 (98.7)
None
A6D, A7D, A8D
6 (98.7), 7 (98.7), 8 (98.7)
None
6 (98.7), 7 (98.7), 8 (98.7)
None
A0D, A1D, A2D
27
0 (98.7), 1 (98.7), 2 (98.7)
None
19
0 (98.7), 1 (98.7), 2 (98.7)
None
A3D, A4D, A5D
3 (98.7), 4 (98.7), 5 (98.7)
None
3 (98.7), 4 (No ID), 5 (98.7)
None
A6D, A7D, A8D
6 (98.7), 7 (98.7), 8 (98.7)
6 → 7 (90.3)
6 (98.7), 7 (98.7), 8 (98.7)
None
T0D
0
No ID
7 (98.7)
0
No ID
3 (98.7), 5 (98.7), 6 (98.7)
T3D
3 (98.7)
None
No ID
1 (97.5), 5 (98.7), 8 (98.6)
T4D
4 (98.7)
1 (98.7), 3 (98.7), 6 (92.9), 7 (98.4)
No ID
None
T5D
No ID
2 (91.4), 7 (98.7)
No ID
None
T6D
6 (98.7)
2 (98.7), 7 (98.7)
No ID
None
S2D
0
2 (98.7)
None
0
—
—
S3D
3 (98.7)
None
—
—
S8D
8 (98.7)
None
—
—
Inupt layer neurons 350; first-hidden-layer neurons 256 and second-hidden-layer neurons 128; output-layer neurons 9; epoch error < 10−15; weight limits ±2.5; AD decimal IR absorption spectrum (Nicolet MX-1 or 10-DX spectrometer measurement);
decimal-first-derivitive absorption spectrum; i = (0, 1, 2, 3, 4, 5, 6, 7, 8) ≡ (VX, Vx, DIMP, DMMP, GA, GB, GD, GF, SF96); TD decimal Gaussian 90 model theoretical prediction spectrum;
decimal-first-derivitive theoretical prediction absorption spectrum; SD decimal interferometer sensor spectrum;
decimal-first-derivitive interferometer sensor spectrum. Left, transfer function gain 1.50, learning rate 0.120; right, transfer function gain 1.00, learning rate 0.200.