Skip to main content

Self-Adapting Soft Sensor for On-Line Prediction.

Kadlec, P. and Gabrys, B., 2009. Self-Adapting Soft Sensor for On-Line Prediction. In: Köppen, M., Kasabov, N. and Coghill, G., eds. Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part I. Heidelberg: Springer, 1172-1179.

Full text available as:

kadlecGabrys-ICONIP_2008.pdf - Accepted Version


Official URL:

DOI: 10.1007/978-3-642-02490-0_142


When it comes to application of computational learning techniques in practical scenarios, like for example adaptive inferential control, it is often difficult to apply the state-of-the-art techniques in a straight forward manner and usually some effort has to be dedicated to tuning either the data, in a form of data pre-processing, or the modelling techniques, in form of optimal parameter search or modification of the training algorithm. In this work we present a robust approach to on-line predictive modelling which is focusing on dealing with challenges like noisy data, data outliers and in particular drifting data which are often present in industrial data sets. The approach is based on the local learning approach, where models of limited complexity focus on partitions of the input space and on an ensemble building technique which combines the predictions of the particular local models into the final predicted value. Furthermore, the technique provides the means for on-line adaptation and can thus be deployed in a dynamic environment which is demonstrated in this work in terms of an application of the presented approach to a raw industrial data set exhibiting drifting data, outliers, missing values and measurement noise.

Item Type:Book Section
Series Name:Lecture Notes in Computer Science
Group:Faculty of Science & Technology
ID Code:8511
Deposited On:19 Dec 2008 19:48
Last Modified:14 Mar 2022 13:19


Downloads per month over past year

More statistics for this item...
Repository Staff Only -