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.
Billings, S.A. and Zhu, Q.M. (1994). Nonlinear model validation using correlation test, International Journal of Control60(6): 1107-1120.10.1080/00207179408921513
Boukhris, A., Mourot, G. and Ragot, J. (1999). Non-linear dynamic system identification: A multiple-model approach, International Journal of Control72(7/8): 591-604.10.1080/002071799220795
Dumitrescu, D., Lazzerini, B. and Jain, L.C. (2000). Fuzzy Setsand Their Application to Clustering and Training, CRC Press Taylor & Francis, Boca Raton, FL.10.1201/9781482273977
Gawthrop, P.J. (1995). Continuous-time local state local model networks, 1995 IEEE Conference on Systems, Man and Cybernetics,Vancouver, Canada, 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, Technical ReportCSC-96005, 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, 2000 IEEE InternationalConference on Intelligent Engineering Systems, INES2000, Portoroz, Slovenia, pp. 167-171.
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.2007.11.004
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/v10006-012-0015-8
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.2003.819824
Johansen, T.A. and Foss, A.B. (1993). Constructing NARMAX using ARMAX models, International Journal of Control58(5): 1125-1153.10.1080/00207179308923046
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.
Kiriakidis, K. (2007). Nonlinear modelling by interpolation between linear dynamics and its application in control, Journal of Dynamics Systems, Measurement and Control129(6): 813-824.10.1115/1.2789473
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): 605-619.10.1080/002071799220803
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.x
Narendra, K.S. and Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks, IEEETransactions on Neural Networks 1(1): 4-27.10.1109/72.80202
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.
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.2008.021000
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.2008.0148
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/v10006-007-0004-5
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, Automatica 31(12): 1691-1724.10.1016/0005-1098(95)00120-8
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.1985.6313399
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.2005.04.015
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)00120-8
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): 1385-1398.10.1080/0020717021000023807
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.
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.2007.03.003
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/v10006-012-0014-9
Yen, J., Wang, L. and Gillespie, C.W. (1998). Improving the interpretability of Takagi-Sugeno fuzzy models by combining global learning and local learning, IEEE Transactionson Fuzzy Systems 6(4): 530-537.10.1109/91.728447