Abstract
To implement on-line process monitoring techniques, a neural network approach to the on-line solution of outlier detection is considered. The first technique is based on the determination of the predictor coefficients on the variables. The coefficients are then solved using least squares (LS) optimization criteria. The second technique is a standard penalty method implemented as an analog neural network. A recursive algorithm is developed for estimating the weights of the ANN and parameters of LS model. The validity and performance of the proposed algorithms has been verified by computer simulation experiments. The analog neural networks are deemed to be particularly well suited for high throughput, real time applications.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yang, T. (2005). Neural Networks for Solving On-Line Outlier Detection Problems. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_73
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DOI: https://doi.org/10.1007/11427469_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)