A hybrid AANN-KPCA approach to sensor data validation

Authors:
Reza Sharifi;Reza Langari
Affiliations:
Department of Mechanical Engineering, College Station, TX;Department of Mechanical Engineering, College Station, TX
Venue:
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
Year:
2007

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Abstract

In this paper two common methods for nonlinear principal component analysis are compared. These two methods are Auto-associative Neural Network (AANN) and Kernel PCA (KPCA). The performance of these methods in sensor data validation are discussed, finally a methodology which takes advantage of both of these methods is presented. The result is a unique approach to nonlinear component mapping of a given set of data obtained from a nonlinear quasi-static system. This method is finally compared with AANN and KPCA for sensor data validation and shows a better performance in terms of predicting/reconstructing the missing or corrupted channels of data.