Abstract
We propose a new radial basis function (RBF) neural network for probability density function estimation. This network is used for detecting changes in multivariate processes. The performance of the proposed model is tested in terms of the average run lengths (ARL), i.e., the average time delays of the change detection. The network allows the processing of large streams of data, memorizing only a small part of them. The advantage of the proposed approach is in the short and reliable net training phase.
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Keywords
- Radial Basis Function
- Control Chart
- Radial Basis Function Neural Network
- Radial Basis Function Network
- Average Time Delay
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Skubalska-Rafajłowicz, E. (2006). RBF Neural Network for Probability Density Function Estimation and Detecting Changes in Multivariate Processes. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_15
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DOI: https://doi.org/10.1007/11785231_15
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