Statistical processes monitoring based on improved ICA and SVDD

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4113 LNCS - I, Issue , 2006, Pages 1247-1256

  Xie, Lei ^a   Kruger, Uwe ^b  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^b QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDARY CONDITIONS; DATA REDUCTION; DISTRIBUTED COMPUTER SYSTEMS; OPTIMIZATION; VECTORS;

NON GAUSSIAN DISTRIBUTED INDEPENDENT COMPONENTS; PARTICLE SWARM OPTIMIZATION (PSO); PROCESS FAULTS; SUPPORT VECTOR DATA DESCRIPTION (SVDD);

INDEPENDENT COMPONENT ANALYSIS;

EID: 33749554418     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11816157_156     Document Type: Conference Paper

References (22)

1. Ku, W., Storet, R. H., Georgakis, C.: Disturbance Detection and Isolation by Dynamic Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems. 30 (1995) 179-196
2. Bakshi, B.R.: Multiscale PCA with Application to Multivariate Statistical Process Monitoring. AIChE J. 44 (1998) 1596-1610
3. Kramer, M.A.: Nonlinear Principal Component Analysis Using Auto-associative Neural Networks. AIChE. J. 37 (1991) 233-243
4. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis, John Wiley & Sons, New York, (2001)
5. Li, R. F., Wang, X. Z.: Dimension Reduction of Process Dynamic Trends Using Independent Component Analysis. Computers and Chemical Engineering. 26 (2002) 467-473
6. Kano, M., Tanaka, S., Hasebe, S.: Monitoring Independent Components for Fault Detection. AIChE. J. 49 (2003) 969-976
7. Himberg, J., Icasso, A. H.: Software for Investigating the Reliability of ICA Estimates by Clustering and Visualization, in: Proc. IEEE Workshop on Neural Networks for Signal Processing (NNSP2003), Toulouse, France (2003) 259-268
8. Hyvärinen, A.: Fast and Robust Fixed-point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks. 10 (1996) 626-634
9. Bell, A., Sejnowski, T.: An Information-maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation. 7 (1995) 1129-1159
10. Kennedy, J., Eberhart, R.: Particle Swarm Optimization, In Proc. IEEE Int. Conf. Neural Networks, Perth, (1995), 1942-1948
11. Parsonpoulos, K.E., Varhatis: Recent Approaches to Global Optimization Problems through Particle Swarm Optimization. Natural Computing. 1 (2002) 235-306
12. Mendes, R.: Population Topologies and their Influence in Particle Swarm Performance. Ph.D thesis, University of Minho, (2004)
13. Johannesmeyer, M. C., Singhal, A., Seborg, D.E.: Pattern Matching in Historical Data. AIChE. J. 48 (2002) 2022-2038
14. Rick, C.: Simple and Fast Linear Space Computation of Longest Common Subsequence. Information Processing Letters. 75 (2000) 275-281
15. Tax, D.M.J., Duin, R.P.W.: Uniform Object Generation for Optimizing One-class Classifiers. Journal of Machine Learning Research 2 (2001) 15-173
16. Schölkopf, B., Somla, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT press, Cambridge, 2002
17. Frauke, F., Igel, C.: Evolutionary Tuning of Multiple SVM Parameters. Neurocomputing 64(2005) 107-117
18. Xie, L.: Swarm Intelligent Tuning of One-class v-SVM Parameters, in: Proc. The First International Conference on Rough Sets and Knowledge Technology, ChongQing, China (2006)
19. Downs, J.J., Vogel, E.F.: A Plant-wide Industrial Process Control Problem. Computers and Chemical Engineering. 17 (1993) 245-255
20. Larsson, T., Hestetun, K., Hovland, E., Skogestad, S.: Self-optimizing Control of A Largescale Plant: the Tennessee Eastman Process. Ind. Rng. Chem. Res. 40 (2001) 4889-4901
21. Kano, M., Nagao, K., Hasebe, S.: Comparison of Multivariate Statistical Process Monitoring Methods with Applications to the Eastman Challenge Problem. Computers and Chemical Engineering. 26 (2002) 161-174
22. Lee, J., Yoo, C., Lee, I.: Statistical Monitoring of Dynamic Processes Based on Dynamic Independent Component Analysis. Chemcial Engineering Science. 59 (2004) 2995-3006