References
Andrew, F. W., Anthony, J. H. & AndrewWare, J., 2007- Two Supervised Neural Networks for Classification of Sedimentary Organic Matter Images from Palynological Preparations, Math Geol, 39: 657–671.
Cybenko, G., 1989- Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), 303.
Exploration of Sonajil copper deposit, Iranian company of copper, northwestern report exploration, 1380-1382.
Funahashi, K. I., 1989- On the approximate realization of continuous mappings by neural networks. Neural Networks, 2(3), 193.
Gallagher, M. R., 1999- Multi-layer perceptron error surfaces: Visualization, structure and modeling: Unpublished PhD dissertation, University of Queensland, 225 p.
Hartman, E. J., Keeler, J. D. & Kowalski, J. M., 1990- Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation, 2(2), 210.
Hassanipak, A. A. & Sharafeddin, M., 2005- Exploration data analysis, Tehran university press, p303.
Hezarkhani, A. & Ghayouri, K., 2007- Ore Forming Fluid geochemistry Investigations on Sonajil Porphyry Stock, Azarbaijan-Iran, Based on Fluid Inclusion Microthermometry. Amirkabir Journal of Science and Technology.
Hezarkhani, A., 2003- Exploration of Sonajil copper deposit, Iranian company of copper, northwestern report exploration.
Hezarkhani, A., 2007- Hydrothermal Evolution in Sonajil Porphyry Copper System (East Azarbaijan Province, Iran): The History of an Uneconomic Deposit. Journal of IGR, Stanford-USA.
Hornik, K., Stinchcombe, M. & White, H., 1989- Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359.
Koike, K. & Matsuda, S., 2003- Characterizing content distributions of impurities in A limestone mine using a feed forward neural network: Nat. Resour. Res., 12, 3: 209–223.
Koike, K., Matsuda, S. & Gu, B., 2001- Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis: Math. Geol., 33, 4: 421–448.
Krose, B. & Smagt, P.,1996- An introduction to neural networks, eighth edition, November, Amsterdam.
Lacassie, J. P., Roser, B. P., Ruiz-del-Solar, J. & Herv´e, F., 2004- Visualization of geochemical datasets by using neural networks: a novel perspective for sedimentary provenance analysis: Sedimentary Geol., 165, 1: 175–191.
Lacassie, J. P., Solar, J. R., Roser, B. & Herve, F., 2006- Visualization of Volcanic Rock Geochemical Data and Classification with Artificial Neural Networks, Mathematical Geology, 38, 6.
Matıas, J. M., Vaamonde, A., Taboada, J. & Gonz´alez-Manteiga, W., 2004- Comparison of Kriging and Neural Networks With Application to the Exploitation of a Slate Mine, Mathematical Geology, 36, 4.
Rizzo, D. M. & Dougherty, D. E., 1994- Characterization of aquifer properties using artificial neural networks: Neural kriging: Water Resour. Res., 30, 2: 483–497.
Samanta, B., Bandopadhyay, S. & 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., 11, 1–4: 69–76.
Samanta, B., Ganguli, R. & Bandopadhyay, S., 2005- Comparing the predictive performance of neural networks with ordinary kriging in a bauxite deposit: Transactions of Institute of Mining and Metallury, 114:129–139
Shang, Yi. & Wah, B. W., 1996- Global optimization for neural network training: IEEE Comput., 29, 3:45–54.
Singer, D. A., & Kouda, R., 1996- Application of a feed forward neural network in the search for Kuroko deposits in the Hokuroku district, Japan: Math. Geol., 28, 8:1017–1023.
Singer, D. A., 2006- Typing mineral deposits using their associated rocks and grades and tonnages in a probabilistic neural network. Math Geol 38(4):465–475.
Weller, A. F., Corcoran, J., Harris, A. J. & Ware, J. A., 2005- The semi-automated classification of sedimentary organic matter in palynological preparations. Comput Geosci 31(10):1213–1223.
Weller, A. F., Harris, A. J., Ware, J. A. & Jarvis, P. S., 2006- Determining the saliency of feature measurements obtained from images of sedimentary organic matter for use in its classification. Comput Geosci 32(9):1357–1367.
Wu, X. & Zhou, Y., 1993- Reserve estimation using neural network techniques: Comput. Geosci., 19, 4:567–575.
Yama, B. R. & Lineberry, G. T., 1999- Artificial neural network application for a predictive task in Mining: Mining Eng., 51, 2:59–64.
)