Nonlinear system identification using heterogeneous multiple models
By: Rodolfo Orjuela, Benoît Marx, José Ragot and Didier Maquin
Open Access
|Mar 2013References
- Abonyi, J. and Babuška, R. (2000). Local and global identification and interpretation of parameters in Takagi-Sugeno fuzzy models,, pp. 835-840.
- Babuška, R. (1998)., Kluwer Academic Publishers, London.
- Billings, S.A. and Zhu, Q.M. (1994). Nonlinear model validation using correlation test,(6): 1107-1120.
- Boukhris, A., Mourot, G. and Ragot, J. (1999). Non-linear dynamic system identification: A multiple-model approach,(7/8): 591-604.
- Dumitrescu, D., Lazzerini, B. and Jain, L.C. (2000)., CRC Press Taylor & Francis, Boca Raton, FL.
- Edwards, D. and Hamson, M. (2001)., 2nd Edn., Basingstoke, Palgrave, Chapter 1, p. 3.
- Filev, D. (1991). Fuzzy modeling of complex systems,(3): 281-290.
- Gatzke, E.P. and Doyle III, F.J. (1999). Multiple model approach for CSTR control,, pp. 343-348.
- Gawthrop, P.J. (1995). Continuous-time local state local model networks,, pp. 852-857.
- 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,, Centre for Systems and Control, Glasgow University, Glasgow.
- Gregorčič, G. and Lightbody, G. (2000). Control of highly nonlinear processes using self-tuning control and multiple/local model approaches,, pp. 167-171.
- Gregorčič, G. and Lightbody, G. (2008). Nonlinear system identification: From multiple-model networks to Gaussian processes,(7): 1035-1055.
- Ichalal, D., Marx, B., Ragot, J. and Maquin, D. (2012). New fault tolerant control strategies for nonlinear Takagi-Sugeno systems,(1): 197-210, DOI: 10.2478/v10006-012-0015-8.
- Johansen, T.A. and Babuška, R. (2003). Multi-objective identification of Takagi-Sugeno fuzzy models,(6): 847-860.
- Johansen, T.A. and Foss, A.B. (1993). Constructing NARMAX using ARMAX models,(5): 1125-1153.
- Kanev, S. and Verhaegen, M. (2006). Multiple model weight estimation for models with no common state,, pp. 637-642.
- Kiriakidis, K. (2007). Nonlinear modelling by interpolation between linear dynamics and its application in control,(6): 813-824.
- Leith, D.J. and Leithead, W.E. (1999). Analytic framework for blended multiple model systems using linear local models,(7): 605-619.
- Ljung, L. (1999)., 2nd Edn., Prentice Hall PTR, London.
- Mäkilä, P.M. and Partington, J.R. (2003). On linear models for nonlinear systems,(1): 1-13.
- McLoone, S. and Irwin, G.W. (2003). On velocity-based local model networks for nonlinear identification,(2): 309-315.
- Murray-Smith, R. and Johansen, T.A. (1997)., Taylor & Francis, London.
- Narendra, K.S. and Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks,(1): 4-27.
- Nelles, O. (2001)., Springer-Verlag, Berlin/Heidelberg.
- Nie, J. (1994). A neural approach to fuzzy modeling,, pp. 2139-2143.
- Orjuela, R., Maquin, D. and Ragot, J. (2006). Nonlinear system identification using uncoupled state multiple-model approach,.
- Orjuela, R., Marx, B., Ragot, J. and Maquin, D. (2008). State estimation for nonlinear systems using a decoupled multiple mode,(1): 59-67.
- 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,(7): 877-890.
- Rodrigues, M., Theilliol, D., Aberkane, S. and Sauter, D. (2007). Fault tolerant control design for polytopic LPV systems,(1): 27-37, DOI: 10.2478/v10006-007-0004-5.
- 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,(12): 1691-1724.
- Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to model and control,(1): 116-132.
- 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,(6): 699-717.
- Venkat, A.N., Vijaysai, P. and Gudi, R.D. (2003). Identification of complex nonlinear processes based on fuzzy decomposition of the steady state space,(6): 473-488.
- Verdult, V., Ljung, L. and Verhaegen, M. (2002). Identification of composite local linear state-space models using a projected gradient search,(16/17): 1385-1398.
- Vinsonneau, B., Goodall, D. and Burnham, K. (2005). Extended global total least square approach to multiple-model identification,, p. 143.
- Walter, E. and Pronzato, L. (1997)., Springer-Verlag, Berlin.
- Wen, C., Wang, S., Jin, X. and Ma, X. (2007). Identification of dynamic systems using piecewise-affine basis function models,(10): 1824-1831.
- Xu, D., Jiang, B. and Shi, P. (2012). Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model,(1): 183-196, DOI: 10.2478/v10006-012-0014-9.
- Yen, J., Wang, L. and Gillespie, C.W. (1998). Improving the interpretability of Takagi-Sugeno fuzzy models by combining global learning and local learning,(4): 530-537.
Language: English
Page range: 103 - 115
Published on: Mar 26, 2013
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
© 2013 Rodolfo Orjuela, Benoît Marx, José Ragot, Didier Maquin, published by University of Zielona Góra
This work is licensed under the Creative Commons License.