Abstract
In the case of a large number of applications, especially complex industrial ones, the knowledge on system’s (process, plant, etc.) parameters during the operation of the system is of major importance. However, in real cases, there are always parameters, which are not accessible. In the present work, we focus our interest around the extraction possibility of information relative to inaccessible parameters. Of course, such dilemma becomes a very complex and difficult problem in a general context. However, we will discuss some realistic (and especially, realizable) conditions for which a solution could be approached. In proposed approach, we use the neural network’s learning and a synaptic weight based indicator to detect changes related to system’s inaccessible parameters. Experimental results relative to a real industrial process have been reported validating our approach.
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.
Similar content being viewed by others
References
M. MAYOUBI, M. SCHAFER, S. SINSEL, “Dynamic Neural Units for Non-linear Dynamic Systems Identification”, From Natural to Artificial Neural Computation, LNCS Vol. 930, Springer Verlag, 1995, pp.1045–1051.
Faller W., Schreck S., “Real-Time Prediction Of Unsteady Aerodynamics: Application for Aircraft Control and Manoeuvrability Enhancement”, IEEE Transactions on Neural Networks, Vol. 6,No6, Nov. 1995.
Y. MAIDON, B.W. JERVIS, N. DUTTON, S. LESAGE, “Multifault Diagnosis of Analogue Circuits Using Multilayer Perceptrons,” IEEE European Test Workshop 96, Montpellier, June 12-14,1996.
I. Grabec, “Continuation of Chaotic Fields by RBFNN”, Lecture Notes in Computer Science-Biological and Artificial Computation: From Neuroscience to Technology, Edited by: Jose Mira, Roberto M. Diaz and Joan Cabestany-Springer Verlag Berlin Heidelberg 1997, pp. 597–606.
A. Sachenko, V. Kochan, V. Turchenko, V. Golovko, J. Savitsky, A. Dunets, T. Laopoulos, “Sensor errors prediction using neural networks”, Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks-IJCNN’2000, Jul 24-Jul 27 2000, 2000, Como, Italy, p 441–446.
A. Sachenko, “IMCS development on the basis of distributed sensor networks”, IEE AFRICON Conference, v1, 1999 IEEE, Piscataway, NJ, USA, p 345–350.
Y. Zhang, Y. MA, “CGHA for Principal Component extraction in the complex domain”, IEEE Trans. On Neural Networks, Vol. 8,No.5, September 1997, pp. 1031–1036.
H.B. Yang, L.C. Jiao, “Neural network for solving generalized eigenvalues of matrix pair”, 2000 IEEE Interntional Conference on Acoustics, Speech, and Signal Processing M1 2000 Jun 5-Jun 9, p 3478–3481.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Madani, K., Berechet, I. (2001). Inaccessible Parameters Monitoring in Industrial Environment: A Neural Based Approach. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_75
Download citation
DOI: https://doi.org/10.1007/3-540-45723-2_75
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42237-2
Online ISBN: 978-3-540-45723-7
eBook Packages: Springer Book Archive