A Hybrid AANN-KPCA Approach to Sensor Data Validation

摘要

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.