Abstract
Data driven computational intelligence methods have become popular in Fault detection and isolation (FDI) due to relatively quick design and not so difficult implementation on real systems. In this paper a research work on a Taguchi DoE approach for training the auto-associative neural network to extract non-linear principal components of a system, is presented. Design of such network was first proposed by Kramer however for achieving robustness to unspecified parameters such as noise level and disturbances, a design of experiments methodology can be used to optimally define network structure and parameters.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Venkatasubramanian, V., Raghunathan, R., Yin, K., Kavuri, N.S.: A review of process fault detection and diagnosis. Computers and Chemical Engineering 27, 293–326, Part I, Part II, Part III (2003)
Uraikul, V., Chan, C.W., Tontiwachwuthikul, P.: Artificial intelligence for monitoring and supervisory control of process systems. Engin. App. of Artificial Intelligence 20, 115–131 (2007)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2004)
Kramer, M.A.: Nonlinear principal component analysis using auto-associative neural networks. AIChE Journal 37, 233–243 (1991)
Hsieh, W.W.: Nonlinear principal component analysis by neural networks. Tellus 53A, 599–615 (2001)
Malthouse, E.C.: Limitations of nonlinear PCA as performed with generic neural networks. IEEE transactions on neural networks 9, 165–173 (1998)
Hines, J.W., Uhrig, R.E., Wrest, D.J.: Use of Autoassociative Neural Networks for Signal Validation. Journal of Intelligent and Robotic Systems 21, 143–154 (1998)
Jia, F., Martin, E.B., Morris, A.J.: Non-linear Principal Components Analysis for Process fault detection. Computers and Chemical Engineering 20, 851–854 (1998)
Kim, Y., Yum, B.: Robust design of multilayer feedforward neural networks: an experimental approach. Engin. App. of Artificial Intelligence 17, 249–263 (2004)
Taguchi, G., Chowdhury, S., Wu, Y.: Taguchi’s Quality Engineering Handbook. John Wiley&Sons, New Jersey (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bratina, B., Muškinja, N., Tovornik, B. (2008). Design of an Auto-associative Neural Network by Using Design of Experiments Approach. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85563-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-85563-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85562-0
Online ISBN: 978-3-540-85563-7
eBook Packages: Computer ScienceComputer Science (R0)