Abstract
Inspired from biological nervous systems and brain structure, Artificial Neural Networks (ANN) could be seen as information processing systems, which allow elaboration of many original techniques covering a large field of applications. Among their most appealing properties, one can quote their learning and generalization capabilities. If a large number of works have concerned theoretical and implementation aspects of ANN, only a few are available with reference to their real world industrial application capabilities. In fact, applicability of an available academic solution in industrial environment requires additional conditions due to industrial specificities, which could sometimes appear antagonistic with theoretical (academic) considerations. The main goal of this paper is to present, through some of main ANN models and based techniques, their real application capability in real industrial dilemmas. Several examples dealing with industrial and real world applications have been presented and discussed covering “intelligent adaptive control”, “fault detection and diagnosis”, “decision support”, “complex systems identification” and “image processing”.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
REFERENCES
Nelles, O., 1995. On the identification with neural networks as series–parallel and parallel models. In ICANN’95, International Conference on Artificial Neural Networks, Paris, France.
Faller W., Schreck S., 1995. Real–Time Prediction Of Unsteady Aerodynamics : Application for Aircraft Control and Manoeuvrability Enhancement. In IEEE Transac. on Neural Networks, Vol. 6, No 6, Nov. 95.
Maidon Y., Jervis B. W., Dutton N., Lesage S., 1996. Multifault Diagnosis of Analogue Circuits Using Multilayer Perceptrons. In IEEE European Test Workshop 96, Montpellier, June 12–14, 1996.
Anderson C.W., Devulapalli S.V., Stolz E.A., 1995. Determining Mental State from EEG Signals Using Parallel Implementations of Neural Networks. In Scientific Programming, Special Issue on Applications Analysis, 4, 3, Fall, pp. 171–183.
Madani K., Bengharbi A., Amarger V., 1997. Neural Fault Diagnosis Techniques for Nonlinear Analog Circuit. In SPIE Vol. 3077, pp 491–502, Orlando, Florida, U.S.A., April 21 to 24, 1997.
Sachenko A., Kochan V., Turchenko V., Golovko V., Savitsky J., Dunets A., Laopoulos T., 2000. Sensor errors prediction using neural networks. In Proceedings IJCNN’2000, Jul 24–Jul 27 2000, Como, Italy, pp. 441–446.
Arbib M.A., 2003. Handbook of Brain Theory and Neural Networks" 2ed. M.I.T. Press. 2003.
Hebb S., 1949. The Organization of Behaviour, Wiley and Sons, New–York, U.S.A., 1949.
Kohonen T., 1984. Self–Organization and Associative Memory, Springer–Verlag, Germany, 1984.
Rumelhart D., Hinton G., Williams R., 1986. Learning Internal Representations by Error Propagation". Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, MA, 1986.
Bigot P., Cosnard M., 1993. Probabilistic Decision Trees and Multilayered Perceptrons. In Proc. of the Europ. Symp. on A. N. N., ESANN’93, pp. 91–96, 1993.
Bogdan M., Speakman H., Rosenstiel W., 1994. Kobold: A neural Coprocessor for Back–propagation with online learning. In Proc. NeuroMicro 94, Torino, Italy, pp. 110–117.
Reyneri L.M., 1995. Weighted Radial Basis Functions for Improved Pattern Recognition and Signal Processing. Neural Processing Letters, Vol. 2, No. 3, pp. 2–6, May 1995.
Trémiolles G., Madani K., Tannhof P., 1996. A New Approach to Radial Basis Function’s like Artificial Neural Networks. In NeuroFuzzy’96, IEEE European Workshop, Vol. 6 N° 2, pp. 735–745, April 16 to 18, Prague, Czech Republic, 1996.
Hormel M.A., 1992. Self–organizing associative memory system for control applications. Advances Neural Information processing Systems 2,Touretzky Ed. Los Altos,CA: Morgan Kaufmann,1992 pp. 332–339.
Miller W.T, 1987. Sensor based control of robotic manipulators using a general learning algorithm, IEEE J. of Robotics and Automation, pp. 157–165.
Madani K., Mercier G., Chebira A., Duchesne S., 1996. Implementation of Neural Network Based Real Time Process Control on IBM Zero Instruction Set Computer. In SPIE AeroSense’96, Applications and Science of Artificial Neural Networks, Orlando, USA, SPIE Proc. Vol. 2760, pp. 250–261.
Albus J.S., 1975. A new Approach to Manipulator Control, Transa of ASME, Sept. 1975 pp. 220–227.
Comoglio R.F, 1992. CMAC Neural network Architecture of Autonomous Undersea Vehicle. In SPIE vol 1709. Applications of artificial Neural Networks III, pp. 517– 527, 1992.
Madani K., Mercier G., Dinarvand M., Depecker JC, 1999. A Neuro–Vector based electrical machines driver combining a neural plant identifier and a conventional vector controller. In SPIE Intern. Symp. AeroSense’99, Orlando, Florida, USA, April 1999.
Trémiolles G., Tannhof P., Plougonven B., Demarigny C., Madani K., 1997. Visual Probe Mark Inspection, using Hardware Implementation of Artificial Neural Networks, in VLSI Production. In LNCS. Biological and Artificial Computation:From Neuroscience to Technology, Ed.: J. Mira, R.M. Diaz and J. Cabestany – Springer Verlag Berlin Heidelberg, pp. 1374–1383, 1997.
Tremiolles G, 1998. Contribution to the theoretical study of neuromimetic models and to their experimental validation: use in industrial applications. Ph.D. Report, University of PARIS XII – Val de Marne.
Madani K., Tremiolles G., Tanhoff P., 2003 – a. Image processing using RBF like neural networks: A ZISC– 036 based fully parallel implementation solving real world and real complexity industrial problems. In Journal of Applied Intelligence N°18, 2003, Kluwer Academic Publishers, pp. 195–231.
Murray–Smith R., Johansen T.A., 1997. Multiple Model Approaches to Modeling and Control. Taylor & Francis Publishers, 1997, ISBN 0–7484–0595–X.
Madani K., Chebira A., Rybnik M., 2003 – b. Data Driven Multiple Neural Network Models Generator Based on a Tree–like Scheduler, LNCS "Computational Methods in Neural Modeling", Ed. J. Mira, J.R. Alvarez – Springer Verlag 2003, ISBN 3–540–40210–1, pp. 382– 389.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Madani, K. (2006). INDUSTRIAL AND REAL WORLD APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS Illusion or reality?. In: BRAZ, J., ARAÚJO, H., VIEIRA, A., ENCARNAÇÃO, B. (eds) INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS I. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4543-3_2
Download citation
DOI: https://doi.org/10.1007/1-4020-4543-3_2
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-4136-5
Online ISBN: 978-1-4020-4543-1
eBook Packages: Computer ScienceComputer Science (R0)