Abstract
In this chapter we address the applicability of artificial neural network and fuzzy logic models to real tasks in industrial environments. For this purpose, we shall present a general methodology which will be outlined by means of a case study which consists in the implementation of a classification/decision engine included in an automatic coin recogniser. This coin recogniser is a part of currently available commercial vending machines. The methodology presented can be considered as divided in three main tasks: database compilation, selection of the proper neural or fuzzy model and implementation. A wide range of models, including classical as well as evolutionary algorithms, has been considered. The experimental results demonstrate that the use of artificial neural and fuzzy models overcomes some of the limitations inherent in the traditional techniques considered when solving this task.
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References
Bellman, R. (1961)Adaptive Control Processes: a Guided TourPrinceton University Press.
Kwok, T.-Y. and Yeung, D.-Y. (1997), “Constructive algorithms for structure learning in feedforward neural networks for regression problems,”IEEE Trans. Neural Networksvol. 8, no. 3, pp. 630–645.
Ash, T. and Cottrell, G. (1995), “Topology modifying neural network algorithms,”Handbook of Brain Theory and Neural NetworksMIT Press, Cambridge, MA, pp. 990–993.
Rumelhart, D.E. and McClelland J.L. (1986)Parallel and Distributed Processing: Explorations in the Microstructure of CognitionMIT Press, Cambridge, MA.
Werbos, P. (1974)Beyond Regression: New Tools for Prediction and Analysis in the Behavioral SciencesPh.D. Thesis, Hardvard University.
Kohonen, T. (1989)Self-Organization and Associative MemorySpringer-Verlag.
Voz, J.-L., Verleysen, M., and Thissen, P. (1995), “Suboptimal Bayesian classification by vector quantization with small clusters,”Proceedings of the European Symposium on Artificial Neural Networks ESANN’95pp. 153–160.
Reilly, D.L., Cooper, L.N., and Elbaum, C. (1982), “A neural model for category learning,”Biological Cyberneticsvol. 45, pp. 35–41.
Sirat, J.A. and Nadal, J.P. (1990), “Neural trees: a new tool for classification,” Technical Report, Laboratoires d’Electronique Philips.
Kohonen, T. (1990), “The self-organizing map,”IEEE Proceedingsvol. 78, no. 9, pp. 1464–1480.
Specht, D.F. (1990), “Probabilistic neural networks,”Neural Networksvol. 3, pp. 109–118.
Frosini, A., Gori, M., and Priami, P. (1996), “A neural model for paper currency recognition and verification,”IEEE Trans. on Neural Networksvol. 7, no. 6, pp. 1482–1490.
Moreno, J.M., Núñez, J.L., Madrenas, J., Cabestany, J., and Laúna, J.R. (1998), “VLSI implementation of a neural decision engine for commercial coin recognisers,”Proceedings of the S `“ International Workshop Mixed Design of Integrated Circuits and Systems MIXDES’98, pp. 379–384.
Moreno, J.M. (1994)VLSI Architectures for Evolutive Neural ModelsPh.D. Thesis, Technical University of Catalunya.
Ma, G.-K. and Taylor, F.J. (1990), “Multiplier policies for digital signal processing,”IEEE ASSP Magazinepp. 6–20.
Zadeh, L.A. (1965), “Fuzzy sets,”Information and Controlvol. 8, pp. 338–353.
Jang, J.-S.R. and Sun, C.-T. (1995), “Neuro-fuzzy modeling and control,”Proceedings of the IEEEvol. 83, pp. 378–406.
Mamdani, E.H. and Assilian, S. (1975), “An experiment in linguistic synthesis with a logic controller,”International Journal of Man-Machine Studiesvol. 7, pp. 1–13.
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Moreno, J.M., Madrenas, J., Cabestany, J. (2001). Commercial Coin Recognisers Using Neural and Fuzzy Techniques. In: Jain, L., De Wilde, P. (eds) Practical Applications of Computational Intelligence Techniques. International Series in Intelligent Technologies, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0678-1_3
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DOI: https://doi.org/10.1007/978-94-010-0678-1_3
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