Automatic Fitting of Gaussian Peaks Using Abductive Machine Learning

IEEE Transactions on Nuclear Science

Volumn 45, Issue 1, 1998, Pages 1-16

  Abdel Aal, R E ^a,b  

^a IEEE   (Saudi Arabia)

^b KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

Author keywords

Abductive networks; Learning systems; Peak fitting; Spectral analysis; Spectroscopy

Indexed keywords

COMPUTATIONAL METHODS; CURVE FITTING; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; REGRESSION ANALYSIS; SPECTRUM ANALYSIS;

ABDUCTIVE NETWORKS; ABDUCTORY INDUCTION MECHANISM (AIM); GAUSSIAN PEAKS; PEAK FITTING;

NUCLEAR RADIATION SPECTROSCOPY;

EID: 0031999602     PISSN: 00189499     EISSN: None     Source Type: Journal    
DOI: 10.1109/23.659550     Document Type: Article

References (29)

1. M. A. Mariscotti, A method for automatic identification of peaks in the presence of background and its application to spectrum analysis, Nucl. Instrum. Methods, vol. 50, pp. 309-320, 1967.
2. G. W. Phillips and K. W. Marlow, Automatic analysis of gamma-ray spectra from germanium detectors, Nucl. Instrum. Methods, vol. 137, pp. 525-536, 1976.
3. H. Machner, Automatic analysis of γ-ray spectra including multiplets, Nucl. Instrum. Methods, vol. A258, pp. 246-249, 1987.
4. R. B. Welsh, F. Gyger, D. T. Jost, H. R. Von Gunten, and U. Krahenbuhl, NEWFIT, A computer program for the analysis of alpha, X-ray, and gamma-ray spectra, Nucl. Instrum. Methods, vol. A269, pp. 615-622, 1988.
5. V. K. Madan, Processing of digital spectral data, in Radiation Detection Systems - Lecture Notes on Pulse Processing Techniques in Radiation Detection and Spectrometry, S. K. Kataria Ed. New Delhi: Oxford/IBH PVT Ltd., 1993, pp. 354-378.
6. P. E. Keller, L. J. Kangas, G. L. Troyer, S. Hashem, and R. T. Kouzes, Nuclear spectral analysis via artificial neural networks for waste handling, IEEE Trans. Nucl. Sci., vol. 42, pp. 709-715, 1995.
7. R. A. Vaughan, Ed., Pattern Recognition and Image Processing in Physics. Edinburgh, U.K.: SUSSP and Hilger, 1991.
8. S. M. Weiss and C. A. Kulikowski, Computer Systems That Learn. San Mateo, CA: Morgan Kaufmann, 1991.
9. R. Duda and P. Hart, Pattern Recognition and Scene Analysis. New York: Wiley, 1973.
10. P. D. Wasserman, Neural Computing: Theory and Practice. New York: Van Nostrand Reinhold, 1989.
11. L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth, 1984.
12. G. J. Montgomery and K. C. Drake, in Proc. SPIE Appl. Artificial Neural Networks Conf., Orlando, FL, 1990, pp. 56-64.
13. S. J. Farlow, The GMDH algorithm, in Self-Organizing Methods in Modeling: GMDH Type Algorithms, S. J. Farlow, Ed. New York: Marcel-Dekker, 1984, pp. 1-24.
14. R. L. Barron, A. N. Mucciardi, F. J. Cook, J. N. Craig, and A. R. Barron, Adaptive learning networks: Development and application in the United States of algorithms related to GMDH, in self-organizing, Methods in Modeling: GMDH Type Algorithms, S. J. Farlow, Ed. New York: Marcel-Dekker, 1984, pp. 25-65.
15. P. Olmos, J. C. Diaz, J. M. Perez, G. Garcia-Belmonte, P. Gomez, and V. Rodellar, Application of neural network techniques in gamma spectroscopy, Nucl. Instrum. Methods, vol. A312, pp. 167-173, 1992.
16. P. Olmos, J. C. Diaz, J. M. Perez, P. Gomez, V. Rodellar, P. Aguayo, A. Bru, G. Garcia-Belmonte, and J. L. de Pablos, A new approach to automatic radiation spectrum analysis, IEEE Trans. Nucl. Sci., vol. 38, pp. 971-975, 1991.
17. R. E. Abdel-Aal and M. N. Al-Haddad, Determination of radioisotopes in gamma-ray spectroscopy using abductive machine learning, Nucl. Instrum. Methods, vol. A391, pp. 275-288, 1997.
18. B. J. Wythoff, S. P. Levine, and S. A. Tomelline, Spectral peak verification and recognition using a multilayered neural network, Anal. Chem., vol. 62, pp. 2702-2709, 1990.
19. T. Akkila, T. Lindblad, B. Lund-Jensen, G. Szekely, and A. Eide, A hardware implementation of an analog neural network for Gaussian peak-fitting, Nucl. Instrum. Methods, vol. A327, pp. 573-579, 1993.
20. R. E. Abdel-Aal and M. A. Elhadidy, A machine-learning approach to modeling and forecasting the minimum temperature at Dhahran, Saudi Arabia, Energy-The Int. J., vol. 19, pp. 739-749, 1994.
21. _, Modeling and forecasting the daily maximum temperature using abductive machine learning, Weather and Forecasting, vol. 10, pp. 310-325, 1995.
22. A. G. Ivakhnenko, Polynomial theory of complex systems, IEEE Trans. Syst. Man Cybern., vol. 1, pp. 364-378, 1971.
23. J. M. Malone, II, Regression without models: Directions in search for structure, in Self-Organizing Methods in Modeling: GMDH Type Algorithms, S. J. Farlow, Ed. New York: Marcel-Dekker, 1984, pp. 67-86.
24. A. R. Barron, Predicted squared error: A criterion for automatic model selection, in Self-Organizing Methods in Modeling: GMDH Type Algorithms, S. J. Farlow, Ed. New York: Marcel-Dekker, 1984, pp. 87-103.
25. C. Charalambous, Conjugate gradient algorithm for efficient training of artificial neural networks, Proc. Inst. Elec. Eng., vol. 139, pt. G, pp. 301-310, 1992.
26. S. Knerr, L. Personnaz, and G. Dreyfus, Single-layer learning revisited: A stepwise procedure for building and training a neural network, in Neurocomputing: Algorithms, Architectures and Applications, F. F. Soulie and J. Herault, Ed. Berlin: Springer-Verlag, 1990, pp. 41-50.
27. AIM User's Manual, AbTech Corp., Charlottesville, VA, 1990.
28. XSYS Reference Manual, Triangle Univ. Nuclear Lab., Duke Univ., Durham, NC, 1990.
29. P. R. Bevington, Data Reduction and Error Analysis for the Physical Sciences. New York: McGraw-Hill, 1969.