Identifying significant parameters for hall-heroult process using general regression neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1821, Issue , 2000, Pages 73-78

  Frost, F ^a   Karri, V ^b  

^a Rio Tinto Aluminium Limited   (Australia)

^b UNIVERSITY OF TASMANIA   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84957884316     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45049-1_9     Document Type: Conference Paper

References (18)

1. GRJOTHEIM, K. and KVANDE, H., “Understanding the Hall-Heroult Process for Production of Aluminium”, Aluminium-Verlag, Dusseldorf, 1986.
2. HAUPIN, W. E., “Principles of Aluminium Electrolysis”, Proc. 124th TMS Annual Meeting, Las Vegas, Feb. 12-16, 1995, pp. 195-203.
3. KENIRY, J., “Outline of the Reduction Process”, Proc. Aluminium Smelting Fundamentals, Course 1, Comalco Aluminium Limited, 1994.
4. BOYER, H. E and HALL, T. L., “Metals Handbook”, Desk Addition, American Society for Metals, 1985.
5. TOMAGO ALUMINIUM, “The Aluminium Production Process”, http://www.tomago.com.au/aluminium.html, Feb. 24, 1999.
6. GRJOTHEIM, K. and WELCH, B. J., “Aluminium Smelter Technology”, AVerlag, 1988.
7. MATHEOU, N., “Electrolyte Control in Aluminium Cell”, Proc. Al. Fund., 1994.
8. CAUDILL, M. and BUTLER, C., “Naturally Intelligent Systems”, MIT, 1990.
9. CAUDILL, M. and BUTLER, C., “Understanding Neural Networks - Computer Explorations”, vol. 1, Massachusetts Institute of Technology, 1992.
10. HERTZ, J., KROGH, A. and PALMER, R. G., “Introduction to the Theory of Neural Computing”, Addison-Wesley Publishing Company, 1991.
11. ZURADA, J. M., “Introduction to Artificial Neural Systems”, West Publishing, 1992.
12. SPECHT, D. F., “General Regression Neural Networks”, Institute of Electrical and Electronic Engineers Transactions on Neural Networks, vol. 2, no. 6, Nov. 1991, pp. 568.
13. MASTERS, T., “Advanced Algorithms for Neural Networks: A C++ Sourcebook”, John Wiley and Sons, 1995.
14. SHAFFER, R., “General Regression Neural Networks”, http://cheml.nrl.navy/~shatter/grnn.html, 1998.
15. SARLE, W., “FAQ for comp.ai.neural-net, What is a GRNN?”, part 2, ftp://ftp.sas.com/pub/neural/FAQ.html, 1997.
16. YU X., LOH, N. K., JULLIEN, G. A. and MILLER, W. C., “Comparisons of Four Learning Algorithms for Training the Multi-Layer Feed Forward Neural Networks with Hard Limiting Neurons”, Neural Networks Theory, IEEE, New York, 1996.
17. MASTERS, T., “Practical Neural Network Recipes in C++”, Academic Press Inc., 1993.
18. FROST, F. and KARRI, V., “Determining the Influence of Input Parameters on BP Neural Network Output Error Using Sensitivity Analysis”, Proc. ICCIMA, New Delhi, INDIA, Sep. 1999.