The assessment of variability of the concentration of chromium in soils with the application of neural networks

Polish Journal of Environmental Studies

Volumn 14, Issue 6, 2005, Pages 743-751

  Gruszczynski S ^a  

^a AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

Author keywords

Chromium; Mixture density networks; Neural networks; Probability neural networks; Soil

Indexed keywords

EID: 28244479347     PISSN: 12301485     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. NOWICKA E., OPRYSZEK Z. Ground observations methods assessment, aiming the heavy metals concentration determination. (In Polish) Master's thesis, AGH-University of Science and Technology, Cracow, June 2000.
2. TRAFAS M. Technology of Running Research and Cartographic Processing of Data Concerning the Contamination of Soils in Mining and Industrial Areas.(In Polish) Technical Report KBN 8 T 12E 007 20, AGH-University of Science and Technology, Cracow 2004.
3. MITASOVA H., MITAS L. Interpolation by regularized spline with tension: I. theory and implementation, 1993. URL citeseer.ist.psu.edu/ mitasova93interpolation.html.
4. MASTERS T. Practical neural networks recipies in C++.(In Polish) Wydawnictwa Naukowo-Techniczne, Warsaw, 1996.
5. HECHT-NIELSEN R. Neurocomputing. Reading: Addison-Wesley Pub. Co., Reading, 1990. ISBN 0-201-09355-3.
6. TADEUSIEWICZ R. Neural networks. (In Polish) Problemy Współczesnej Nauki Techniki. Informatyka. Akademicka Oficyna Wydawniczia RM, Warsaw, 1993.
7. ZURADA J., BARSKI M., JEDRUCH W. Artificial neural networks. (In Polish) Wydawnictwo Naukowe PWN, Warszawa, 1996.
8. DUCH W., JANKOWSKI N. Survey of neural transfer functions, 1999. URL citeseer.ist.psu.edu/duch99survey.html.
9. BISHOP C. Improving the generalization properties of radical basis function neural networks. Neural Computation, 3, (4), 579, 1991.
10. PARZEN E. On the estimation of a probability density function and mode. Annals of Mathematical Statistics, 33, 1065, 1962. URL citeseer.ist.psu.edu/parzen62estimation.html.
11. SPECHT D. F. A generalized regression neural network. IEEE Transactions on Neural Networks, 2, 568, November 1991.
12. BISHOP C. M. Mixture density networks. Technical Report NCRG/94/004, Neural Computing Research Grup, Aston University, Birmingham B4 7ET, February 1994. URL http://www.ncrg.aston.ac.uk.
13. GOLDBERG P. W., WILLIAMS C. K. I, BISHOP C. M. Regression with input-dependent noise: A gaussian process treatment. In Michael I. Jordan, Michael J. Kearns, and Sara A. Solla, editors, Advances in Neural Information Processing Systems, volume 10. The MIT Press, 1998. URL citeseer.ist.psu.edu/article/goldberg98regression.html.
14. CORNFORD D., NABNEY I. T., BISHOP C. M. Neural network based wind vector retrieval from satellite scaterrometer data. Neural Computing and Application, 8, 206, 1999.
15. EVANS D. J. Mixture density network training by computation in parameter space. Technical Report NCRG/98/016, Neural Computing Research Grup, Aston University, Birmingham B4 7ET, August 1998. URL http:// www.ncrg.aston.ac.uk.
16. LARK R. M. Designing sampling grids from imprecise information on soil variability, an approach based on the fuzzy kriging variance. Geoderma, 98, 35, April 2000.
17. MITASOVA H., BROWN W. M., HOFIERKA J. Multidimensional dynamic cartography. URL citeseer.ist.psu.edu/mitasova95multidimensional.html.