Comparative evaluation of neural network learning algorithms for ore grade estimation

Mathematical Geology

Volumn 38, Issue 2, 2006, Pages 175-197

  Samanta, B ^a   Bandopadhyay, S ^a   Ganguli, R ^a  

^a UNIVERSITY OF ALASKA FAIRBANKS   (United States)

Author keywords

Activation functions; Global learning algorithm; Local learning algorithm; Multimodal; Neural network optimization; Ward net network

Indexed keywords

BAUXITE DEPOSITS; ESTIMATION; GOLD; IRON ORES; LEARNING ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION; SIMULATED ANNEALING;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; COMPARATIVE STUDY; MODELING; ORE GRADE;

EID: 33745326805     PISSN: 08828121     EISSN: 15738868     Source Type: Journal    
DOI: 10.1007/s11004-005-9010-z     Document Type: Article

References (18)

1. Bishop, C. M., 1995, Neural networks for pattern recognition: Clarendon Press, Oxford, 482 p.
2. Gallagher, M. R., 1999, Multi-layer perceptron error surfaces: Visualization, structure and modeling: Unpublished PhD dissertation, University of Queensland, 225 p.
3. Hagan, M. T., Demuth, H. B., and Beale, M., 1996, Neural network design: PWS Publishing Company, Boston, 19 chapters.
4. Haykins, S., 1999, Neural networks: A comprehensive foundation, 2nd ed: Prentice Hall, New Jersey, 824 p.
5. Ke, J., 2002, Neural network modeling of placer ore grade spatial variability: Unpublished Doctoral Dissertation, University of Alaska Fairbanks, 251 p.
6. Kirkpatrick, S., Gerlatt, C. D., Jr., and Vecchi, M. P., 1983, Optimization by simulated annealing: Science, 220, p. 671-680.
7. Koike, K., and Matsuda, S., 2003, Characterizing content distributions of impurities in A limestone mine using a feed forward neural network: Nat. Resour. Res., v. 12, no. 3, p. 209-223.
8. Koike, K., Matsuda, S., and Gu, B., 2001, Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis: Math. Geol., 33, no. 4, p. 421-448.
9. Koike, K., Matsuda, S., Suzuki, T., and Ohmi, M., 2002, Neural network-based estimation of principal metal contents in the Hokuroku district, Northern Japan, for exploring Kuroko-type deposits: Nat. Resour. Res., v. 11, no. 2, p. 135-156.
10. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E., 1958, Equations of state calculations by fast computing machines: J. Chem. Phys., v. 21, p.1087-1092.
11. Pincus, M., 1970, A Monte Carlo method for the approximate solution of certain types of constrained optimization problems, Oper. Res., v. 18, p. 1225-1228.
12. Rizzo, D. M., and Dougherty, D. E., 1994, Characterization of aquifer properties using artificial neural networks: Neural kriging: Water Resour. Res., v. 30, no. 2, p. 483-497.
13. Samanta, B., Bandopadhyay, S., and Ganguli, R., 2004, Data segmentation and genetic algorithms for sparse data division in Nome placer gold grade estimation using neural network and geostatistics: Mining Exploration Geol., v. 11, nos. 1-4, p. 69-76.
14. Samanta, B., Ganguli, R., and Bandopadhyay, S., 2003, Comparing the predictive performance of neural networks with ordinary kriging in a bauxite deposit: Transactions of Institute of Mining and Metallury, UK. (Under review).
15. Shang, Yi, and Wah, B. W., 1996, Global optimization for neural network training: IEEE Comput., v. 29, no. 3, p. 45-54.
16. Singer, D. A., and Kouda, R., 1996, Application of a feed forward neural network in the search for Kuroko deposits in the Hokuroku district, Japan: Math. Geol., v. 28, no. 8, p. 1017-1023.
17. Wu, X., and Zhou, Y., 1993, Reserve estimation using neural network techniques: Comput. Geosci., v. 19, no. 4, p. 567-575.
18. Yama, B. R., and Lineberry, G. T., 1999, Artificial neural network application for a predictive task in Mining: Mining Eng., v. 51, no. 2, p. 59-64.