Nonlinear system identification using heterogeneous multiple models

Abonyi, J. and Babuška, R. (2000). Local and global identification and interpretation of parameters in Takagi-Sugeno fuzzy models, 9th IEEE InternationalConference on Fuzzy Systems, FUZZ-IEEE, San Antonio,CA, USA, pp. 835-840.Search in Google Scholar

Babuška, R. (1998). Fuzzy Modeling for Control, Kluwer Academic Publishers, London.10.1007/978-94-011-4868-9Search in Google Scholar

Billings, S.A. and Zhu, Q.M. (1994). Nonlinear model validation using correlation test, International Journal of Control60(6): 1107-1120.10.1080/00207179408921513Search in Google Scholar

Boukhris, A., Mourot, G. and Ragot, J. (1999). Non-linear dynamic system identification: A multiple-model approach, International Journal of Control72(7/8): 591-604.10.1080/002071799220795Search in Google Scholar

Dumitrescu, D., Lazzerini, B. and Jain, L.C. (2000). Fuzzy Setsand Their Application to Clustering and Training, CRC Press Taylor & Francis, Boca Raton, FL.10.1201/9781482273977Search in Google Scholar

Edwards, D. and Hamson, M. (2001). Guide to MathematicalModelling, 2nd Edn., Basingstoke, Palgrave, Chapter 1, p. 3.Search in Google Scholar

Filev, D. (1991). Fuzzy modeling of complex systems, InternationalJournal of Approximate Reasoning 5(3): 281-290.10.1016/0888-613X(91)90013-CSearch in Google Scholar

Gatzke, E.P. and Doyle III, F.J. (1999). Multiple model approach for CSTR control, 14th IFAC World Congress, Beijing,China, pp. 343-348.Search in Google Scholar

Gawthrop, P.J. (1995). Continuous-time local state local model networks, 1995 IEEE Conference on Systems, Man and Cybernetics,Vancouver, Canada, pp. 852-857.Search in Google Scholar

Gray, G.J., Murray-Smith, D.J., Li, Y. and Sharman, K.C. (1996). Nonlinear system modelling using output error estimation of a local model network, Technical ReportCSC-96005, Centre for Systems and Control, Glasgow University, Glasgow.Search in Google Scholar

Gregorčič, G. and Lightbody, G. (2000). Control of highly nonlinear processes using self-tuning control and multiple/local model approaches, 2000 IEEE InternationalConference on Intelligent Engineering Systems, INES2000, Portoroz, Slovenia, pp. 167-171.Search in Google Scholar

Gregorčič, G. and Lightbody, G. (2008). Nonlinear system identification: From multiple-model networks to Gaussian processes, Engineering Applications of Artificial Intelligence21(7): 1035-1055.10.1016/j.engappai.2007.11.004Search in Google Scholar

Ichalal, D., Marx, B., Ragot, J. and Maquin, D. (2012). New fault tolerant control strategies for nonlinear Takagi-Sugeno systems, International Journal of AppliedMathematics and Computer Science 22(1): 197-210, DOI: 10.2478/v10006-012-0015-8.10.2478/v10006-012-0015-8Search in Google Scholar

Johansen, T.A. and Babuška, R. (2003). Multi-objective identification of Takagi-Sugeno fuzzy models, IEEETransactions on Fuzzy Systems 11(6): 847-860.10.1109/TFUZZ.2003.819824Search in Google Scholar

Johansen, T.A. and Foss, A.B. (1993). Constructing NARMAX using ARMAX models, International Journal of Control58(5): 1125-1153.10.1080/00207179308923046Search in Google Scholar

Kanev, S. and Verhaegen, M. (2006). Multiple model weight estimation for models with no common state, 6th IFACSymposium on Fault Detection, Supervision and Safetyof Technical Processes, SAFEPROCESS, Beijing, China, pp. 637-642.Search in Google Scholar

Kiriakidis, K. (2007). Nonlinear modelling by interpolation between linear dynamics and its application in control, Journal of Dynamics Systems, Measurement and Control129(6): 813-824.10.1115/1.2789473Search in Google Scholar

Leith, D.J. and Leithead, W.E. (1999). Analytic framework for blended multiple model systems using linear local models, International Journal of Control 72(7): 605-619.10.1080/002071799220803Search in Google Scholar

Ljung, L. (1999). System Identification: Theory for the User, 2nd Edn., Prentice Hall PTR, London.Search in Google Scholar

Mäkilä, P.M. and Partington, J.R. (2003). On linear models for nonlinear systems, Automatica 39(1): 1-13.10.1016/S0005-1098(02)00183-8Search in Google Scholar

McLoone, S. and Irwin, G.W. (2003). On velocity-based local model networks for nonlinear identification, Asian Journalof Control 5(2): 309-315.10.1111/j.1934-6093.2003.tb00122.xSearch in Google Scholar

Murray-Smith, R. and Johansen, T.A. (1997). Multiple ModelApproaches to Modelling and Control, Taylor & Francis, London.Search in Google Scholar

Narendra, K.S. and Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks, IEEETransactions on Neural Networks 1(1): 4-27.10.1109/72.80202Search in Google Scholar

Nelles, O. (2001). Nonlinear System Identification, Springer-Verlag, Berlin/Heidelberg.10.1007/978-3-662-04323-3Search in Google Scholar

Nie, J. (1994). A neural approach to fuzzy modeling, AmericanControl Conference, ACC, Baltimore, MD, USA, pp. 2139-2143.Search in Google Scholar

Orjuela, R., Maquin, D. and Ragot, J. (2006). Nonlinear system identification using uncoupled state multiple-model approach, Workshop on Advanced Control and Diagnosis,ACD’2006, Nancy, France.Search in Google Scholar

Orjuela, R., Marx, B., Ragot, J. and Maquin, D. (2008). State estimation for nonlinear systems using a decoupled multiple mode, International Journal of Modelling Identificationand Control 4(1): 59-67.10.1504/IJMIC.2008.021000Search in Google Scholar

Orjuela, R., Marx, B., Ragot, J. and Maquin, D. (2009). On the simultaneous state and unknown inputs estimation of complex systems via a multiple model strategy, IET ControlTheory & Applications 3(7): 877-890.10.1049/iet-cta.2008.0148Search in Google Scholar

Rodrigues, M., Theilliol, D., Aberkane, S. and Sauter, D. (2007). Fault tolerant control design for polytopic LPV systems, International Journal of Applied Mathematicsand Computer Science 17(1): 27-37, DOI: 10.2478/v10006-007-0004-5.10.2478/v10006-007-0004-5Search in Google Scholar

Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P., Hjalmarsson, H. and Juditsky, A. (1995). Nonlinear black-box modeling in system identification: A unified overview, Automatica 31(12): 1691-1724.10.1016/0005-1098(95)00120-8Search in Google Scholar

Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to model and control, IEEE Transactions on Systems, Man, and Cybernetics15(1): 116-132.10.1109/TSMC.1985.6313399Search in Google Scholar

Uppal, F.J., Patton, R.J. and Witczak, M. (2006). A neuro-fuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem, ControlEngineering Practice 14(6): 699-717.10.1016/j.conengprac.2005.04.015Search in Google Scholar

Venkat, A.N., Vijaysai, P. and Gudi, R.D. (2003). Identification of complex nonlinear processes based on fuzzy decomposition of the steady state space, Journal ofProcess Control 13(6): 473-488.10.1016/S0959-1524(02)00120-8Search in Google Scholar

Verdult, V., Ljung, L. and Verhaegen, M. (2002). Identification of composite local linear state-space models using a projected gradient search, International Journal of Control75(16/17): 1385-1398.10.1080/0020717021000023807Search in Google Scholar

Vinsonneau, B., Goodall, D. and Burnham, K. (2005). Extended global total least square approach to multiple-model identification, 16th IFAC World Congress, Prague, CzechRepublic, p. 143.Search in Google Scholar

Walter, E. and Pronzato, L. (1997). Identification of ParametricModels: From Experimental Data, Springer-Verlag, Berlin.Search in Google Scholar

Wen, C., Wang, S., Jin, X. and Ma, X. (2007). Identification of dynamic systems using piecewise-affine basis function models, Automatica 43(10): 1824-1831.10.1016/j.automatica.2007.03.003Search in Google Scholar

Xu, D., Jiang, B. and Shi, P. (2012). Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model, International Journal of AppliedMathematics and Computer Science 22(1): 183-196, DOI: 10.2478/v10006-012-0014-9.10.2478/v10006-012-0014-9Search in Google Scholar

Yen, J., Wang, L. and Gillespie, C.W. (1998). Improving the interpretability of Takagi-Sugeno fuzzy models by combining global learning and local learning, IEEE Transactionson Fuzzy Systems 6(4): 530-537.10.1109/91.728447Search in Google Scholar