Abstract
While there exists a broad range of neural networks for a particular task, different neural network architectures are selected depending upon the nature of application in industry. The range of applications covers anything from performance estimation and pattern recognition to process modelling and control. The network selection can be carried out based on economic considerations, such as cost associated with neural network computation time and obtaining data for required model variables. While each of the selected models can be a possible solution, depending upon the performance criteria, they all can be ranked from most suitable to least suitable for a particular application. In this paper, appraisal of neural networks for three industrial applications, involving process modelling of reduction cells for aluminium production, is discussed. Regression analysis techniques and six neural network models are assessed for their performance, using specific assessment criteria. It is shown that there is no single model that is most appropriate for each of the assessment criteria considered in each instance, hence, the decision of which neural network model is most suitable for a specific application is complex, particularly as the assessment criteria are not fundamentally of equal significance. It is shown that optimisation techniques are necessary to select an appropriate model for an application.
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References
Caudill, M. and Butler, C., “Naturally Intelligent Systems”, Massachusetts Institute of Technology, 1990.
Caudill, M. and Butler, C., “Understanding Neural Networks-Computer Explorations”, vol. 1, Massachusetts Institute of Technology, 1992.
Hertz, J., Krogh, A. and Palmer, R. G., “Introduction to the Theory of Neural Computing”, Addison-Wesley Publishing Company, 1991.
Zurada, J. M., “Introduction to Artificial Neural Systems”, West Publishing Company, 1992.
Karri, V. and Frost, F., “Optimum Backpropagation Network Conditions with Respect to Computation Time and Output Accuracy”, Proc. International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), Sep. 1999, New Delhi, India, pp. 50–54.
Karri, V., “RBF Neural Networks For Thrust and Torque Predictions in Drilling Operations”, Proc. International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), Sep. 1999, New Delhi, India, pp. 55–60.
Frost, F. and Karri, V., “Performance Comparison of BP and GRNN Models of the Neural Network Paradigm Using a Practical Industrial Application”, Proc. 6th International Conference on Neural Information Processing (ICONIP), Nov. 1999, Perth., pp 1069–1075.
Karri, V. and Frost, F., “Effect of Altering the Gaussian Function Receptive Field Width in RBF Neural Networks on Aluminium Fluoride Prediction in Industrial Reduction Cells”, Proc. 6th International Conference on Neural Information Processing (ICONIP), Nov. 1999, Perth., pp 101–106.
Frost, F. and Karri, V., “Intelligent Control of Aluminium Reduction Cells Using Backpropagation Neural Networks”, Proc. International Conference on Advances in Intelligent Systems: Theory and Applications (AISTA), Feb. 2000, Canberra, Australia, pp. 350–356.
Karri, V. and Frost, F., “Combined Kohonen and RBF Networks to Predict Electrolyte Additives in Hall-Heroult Cell”, Proc. International Conference on Advances in Intelligent Systems: Theory and Applications (AISTA), Feb. 2000, Canberra, Australia, pp. 19–24.
Sarle, W., “How to Measure Importance of Inputs”, ftp://ftp.sas.com/pub/neural/FAQ.html, Apr. 24,1999.
Moore, D. S. and McCabe, G. P., “Introduction to the Practice of Statistics”, W. H. Freeman and Company, 1989.
Rumelhart, D. E. and McClelland, J. L., “Parallel Distributed Processing: Explorations in the Microstructure of Cognition”, vol. 1, Cambridge: The MIT Press, 1988.
Khanna, T., “Foundations of Neural Networks”, Massachusetts: Addison-Wesley, 1990.
Song, X. M., “Radial Basis Function Networks”, http://www.cs.helsinki.fi/~xianming/thesis/m_conten.html, 13th Oct. 1998.
Lowe, D., “Radial Basis Function Networks”, Neural Computing Research Group, Aston University, Aston Triangle, Birmingham, 1988, pp. 1–14.
Lowe, D., “Radial Basis Function Networks and Statistics”, Neural Computing Research Group, Aston University, Aston Triangle, Birmingham, 1988, pp. 1–32.
Broomhead, D. S. and Lowe, D., “Multi-Variable Functional Interpolation and Adaptive Networks”, Complex Systems 2, 1988, pp. 321–355.
Kosko, B., “Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence”, Prentice-Hall, Inc., 1992.
Kohonen, T., “Self-Organisation and Associative Memory”, Berlin, Springer-Verlag, 1984.
Kohonen, T., “Adaptive, Associative and Self-Organisation Functions in Neural Computing”, Applied Optics, vol. 26, 1987, pp. 4910–4918.
Kohonen, T., “Self-Organised Formation of Topologically Correct Feature Maps”, Biological Cybernetics, vol. 43, 1982, pp. 59–69.
Kohonen, T., “An Introduction to Neural Computing”, Neural Networks, vol. 1, 1988, p. 4.
Specht, D. F., “General Regression Neural Networks”, Institute of Electrical and Electronic Engineers Transactions on Neural Networks, vol. 2, no. 6, Nov. 1991, pp. 568–576.
Masters, T., “Advanced Algorithms for Neural Networks: A C++ Sourcebook”, John Wiley and Sons, 1995.
Shaffer, R., “General Regression Neural Networks”, http://cheml.nrl.navy/~shatter/grnn.html, 1998.
Sarle, W., “FAQ for comp.ai.neural-net, What is a GRNN?”, part 2, ftp://ftp.sas.com/pub/neural/FAQ.html, 1997.
Grjotheim, K. and Kvande, H., “Understanding the Hall-Heroult Process for Production of Aluminium”, Aluminium-Verlag, Dusseldorf, 1986.
Haupin, W. E., “Principles of Aluminium Electrolysis”, Proc. 124th TMS Annual Meeting, Las Vegas, Feb. 12–16, 1995, pp. 195–203.
Grjotheim, K. and Welch, B. L, “Aluminium Smelter Technology”, Aluminium-Verlag, 1988.
Matheou, N., “Electrolyte Control in Aluminium Cell”, Proc. Al. Fund., 1994.
Huang, S. H. and Zhang, H. C, “Artificial Neural Networks in Manufacturing: Concepts, Applications and Perspectives”, Institute of Electrical and Electronic Engineers Transactions on Components, Packaging and Manufacturing Technology, pt. A, vol. 17, no. 2, 1994, pp. 212–228.
Frost, F. and Karri, V., “Determining the Influence of Input Parameters on BP Neural Network Output Error Using Sensitivity Analysis”, Proc. International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), Sep. 1999, New Delhi, India, pp. 45–49.
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Karri, V., Frost, F. (2000). Need for Optimisation Techniques to Select Neural Network Algorithms for Process Modelling of Reduction Cell. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_49
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DOI: https://doi.org/10.1007/3-540-44533-1_49
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