Total least squares in fuzzy system identification: An application to an industrial engine

Engineering Applications of Artificial Intelligence

Volumn 21, Issue 8, 2008, Pages 1277-1288

  Jakubek, Stefan ^a   Hametner, Christoph ^b   Keuth, Nikolaus ^a  

^a AVL LIST GMBH   (Austria)

^b VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Gas engine; Identification algorithms; Local model networks; Nonlinear system identification; Steady state constraints; Takagi Sugeno fuzzy models; Total least squares

Indexed keywords

CURVE FITTING; DYNAMICAL SYSTEMS; ELECTRIC POWER PLANTS; FEEDBACK CONTROL; FUZZY LOGIC; GAS ENGINES; GEODESY; IDENTIFICATION (CONTROL SYSTEMS); INDUSTRIAL APPLICATIONS; INTERNAL COMBUSTION ENGINES; LEAST SQUARES APPROXIMATIONS; MODEL STRUCTURES; NETWORK PROTOCOLS; NONLINEAR SYSTEMS; PARAMETER ESTIMATION; POWER PLANTS; SCHRODINGER EQUATION; SENSOR NETWORKS;

IDENTIFICATION ALGORITHMS; LOCAL MODEL NETWORKS; NONLINEAR SYSTEM IDENTIFICATION; STEADY-STATE CONSTRAINTS; TAKAGI-SUGENO FUZZY MODELS; TOTAL LEAST SQUARES;

MATHEMATICAL MODELS;

EID: 54049155041     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2008.04.020     Document Type: Article

References (26)

1. Aach, T., Dümbgen, L., Mester, R., 2001. Bayesian illumination invariant change detection using a total least squares test statistic. In: Actes/Proceedings 18e Colloque GRETSI sur le Traitement du Signal et des Images, Toulouse, France, pp. 587-590.
2. Abonyi, J., Babuska, R., Setnes, M., Verbruggen, H., Szeifert, F., 1999. Constrained parameter estimation in fuzzy modeling. In: IEEE International Fuzzy Systems Conference Proceedings. IEEE, New York, pp. 951-956.
3. Abonyi, J., Babuska, R., Szeifert, F., 2001. Fuzzy modeling with multivariate membership functions: gray-box identification and control design. IEEE Transactions on Systems, Man and Cybernetics, Part B 31(5), 755-767.
4. Abonyi, J., Babuska, R., Szeifert, F., 2002. Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man and Cybernetics 32, 612-621.
5. Aguirre L., Barroso M., Saldanha R., and Mendes E. Imposing steady-state performance on identified nonlinear polynomial models by means of constrained parameter estimation. IEE Proceedings on Control Theory and Applications 151 2 (2004) 174-179
6. Babuska R. Fuzzy Modeling for Control (1998), Kluwer Academic Publishers, Norwell, MA, USA
7. Balle, P., Juricic, D., Rakar, A., Ernst, S., 1997. Identification of nonlinear processes and model based fault isolation using local linear models. In: Proceedings of the 1997 American Control Conference 1, pp. 47-51.
8. De Groen P. An introduction to total least squares?. Nieuw Archief Voor Wiskunde 14 2 (1996) 237-254. http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:math/9805076 〈http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:math/9805076〉
9. Draper, N.R., Smith, H., 1998. Applied Regression Analysis, third ed. Wiley Series in Probability and Statistics. Wiley, New York.
10. Golub G., and Loan C.V. An analysis of the total least squares problem. SIAM Journal of Numerical Analysis 17 3 (1980) 883-893
11. Heij C., and Scherrer W. Consistency of system identification by global total least squares. Automatica 35 (1999) 993-1008
12. Hunt, K., 1992. Induction of decision trees for rule-based modelling and control. In: Proceedings of the 1992 IEEE International Symposium on Intelligent Control. IEEE, New York, pp. 306-311.
13. Jakubek S., and Keuth N. A local neuro-fuzzy network for high-dimensional models and optimisation. Engineering Applications of Artificial Intelligence 19 (2006) 705-717
14. Johansen T.A., and Babuska R. Multiobjective identification of Takagi Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems 11 (2003) 847-860
15. Johansen T.A., Hunt K.J., and Murray-Smith R. Off-equilibrium linearisation and design of gain-scheduled control with application to vehicle speed control. Control Engineering Practice 6 (1998) 167-180
16. Johansen T.A., Shorten R., and Murray-Smith R. On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems 8 3 (2000) 297-313
17. Kozek, M., Jovanovic, N., 2002. Identification of Wiener/Hammerstein nonlinear systems with extended Kalman filters. In: Proceedings of the IEEE American Control Conference ACC02, vol. 2, pp. 969-974.
18. Kukolj D., and Levi E. Identification of complex systems based on neural and Takagi-Sugeno fuzzy model. IEEE Transactions on Systems, Man and Cybernetics, Part B 34 (2004) 272-282
19. Maertens K., Johansen T.A., and Babuska R. Engine load prediction in off-road vehicles using multi-objective nonlinear identification. Control Engineering Practice 12 (2004) 615-624
20. Markovsky I., and Huffel S.V. Overview of total least-squares methods. Signal Processing 87 10 (2007) 2283-2302
21. Markovsky I., Willems J., Van Huffel S., De Moor B., and Pintelon R. Application of structured total least squares for system identification and model reduction. IEEE Transactions on Automatic Control 50 (2005) 1490-1500
22. Nelles, O., 1997. LOLIMOT-Lokale, lineare Modelle zur Identifikation nichtlinearer, dynamischer Systeme. Automatisierungstechnik (at) 45 (4), 163-174.
23. Nelles O. Nonlinear System Identification. first ed. (2002), Springer, Berlin
24. Nepomuceno G., Takahashiy H.C., Amaral F.V., and Aguirre L. Nonlinear identification using prior knowledge of fixed points: a multiobjective approach. International Journal of Bifurcation and Chaos 13 (2003) 1229-1246
25. Roorda B. Algorithms for global total least squares modelling of finite multivariable time series. Automatica 31 3 (1995) 391-404
26. Zimmerschied R., Weber M., and Isermann R. Static and dynamic measurements of combustion engines for optimisation of control mappings-a brief survey. Automatisierungstechnik-at 53 (2005) 87-94