A hybrid AANN-KPCA approach to sensor data validation

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.

[1]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[2]  Christopher J. Taylor,et al.  The use of kernel principal component analysis to model data distributions , 2003, Pattern Recognit..

[3]  Ronald Graham,et al.  SIMULATION OF AN ENGINE SENSOR VALIDATION SCHEME USING AN AUTOASSOCIATIVE NEURAL NETWORK , 1997 .

[4]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[5]  J. W. Hines,et al.  Plant wide sensor calibration monitoring , 1996, Proceedings of the 1996 IEEE International Symposium on Intelligent Control.

[6]  Jin Hyun Park,et al.  Fault detection and identification of nonlinear processes based on kernel PCA , 2005 .

[7]  Shufeng Tan,et al.  Reducing data dimensionality through optimizing neural network inputs , 1995 .

[8]  Hiroshi Shimizu,et al.  Data preprocessing and output evaluation of an autoassociative neural network model for online fault detection in virginiamycin production. , 2002, Journal of bioscience and bioengineering.

[9]  Shigeo Abe,et al.  KPCA-based training of a kernel fuzzy classifier with ellipsoidal regions , 2004, Int. J. Approx. Reason..

[10]  In-Beum Lee,et al.  Sensor fault identification based on kernel principal component analysis , 2004, Proceedings of the 2004 IEEE International Conference on Control Applications, 2004..

[11]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  G.W. Irwin,et al.  Industrial process monitoring using nonlinear principal component models , 2004, 2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings (IEEE Cat. No.04EX791).

[14]  Zhihuan Song,et al.  Fault detection behavior and performance analysis of principal component analysis based process monitoring methods , 2002 .

[15]  Weihua Li,et al.  Isolation enhanced principal component analysis , 1999 .

[16]  Metin Demiralp,et al.  Proceedings of the 9th WSEAS international conference on Applied informatics and communications , 2004 .

[17]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[18]  Ten-Huei Guo,et al.  Using Neural Networks for Sensor Validation , 1998 .

[19]  Weiguo Gong,et al.  Feature Selection Based on KPCA, SVM and GSFS for Face Recognition , 2005, ICAPR.

[20]  Cairong Zou,et al.  Face Recognition Based on PCA/KPCA Plus CCA , 2005, ICNC.

[21]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[22]  B. Bakshi.  Multiscale PCA with application to multivariate statistical process monitoring , 1998 .

[23]  Qijun Zhao,et al.  GA-Driven LDA in KPCA Space for Facial Expression Recognition , 2005, ICNC.

[24]  S. Joe Qin,et al.  Subspace approach to multidimensional fault identification and reconstruction , 1998 .