eae7e2f5-745d-427f-8d00-2cafa6263699 beie director@blueeyesintelligence.org 10.35940/ijitee.I9290.0710921 Abonyi J. Feil,B., (2007). Cluster Analysis for Data Mining and System Identification. Birkhauser Verlag AG Berlin.10.1109/TSMCB.2002.1033180Abonyi J., Babuska R. and Szeifert, F., (2002). Modified Gath-Geva Fuzzy Clustering for Identification of Takagi-Sugeno Fuzzy Models, IEEE Transactions on Systems, Man and Cybernetics,Vol. 32.Abonyi J., Chovan T., Szeifert F., (2001). Identification of Nonlinear Systems using Gaussian Mixture of Local Models. Hungarian Journal of Industrial Chemistry. Vol. 29, pages 134-139.Babuska R., van der Veen P.J., Kaymak U., (2002). Improved covariance estimation for gustafson-kessel clustering. In Proceedings of FUZZY-IEEE, pp:1081-1085.Babuska R. and Verbruggen H. B., (1997). Fuzzy sets methods for local modelling and control. Taylor and Francis.Babuska R., (1996). Modeling and Identification. PhD thesis, Dept. of Control Engineering, Delft University of Technology, Delft, The Netherlands.10.1109/91.493905Bensaid A.M., Hall L.O., Bezdek J.C, Clarke L.P., Silbiger M.L., Arrington J.A., and Murtagh R.F., (1996). Validity-guided (Re) Clustering with applica-tions to imige segmentation. IEEE Transactions on Fuzzy Systems, 4:112 -123.Plenum Press New YorkBezdek198110.1007/978-1-4757-0450-1Pattern Recognition With Fuzzy Objective Function AlgorithmsBezdek J.C., (1981). Pattern Recognition With Fuzzy Objective Function Algorithms. Plenum Press,New York.Nonlinear System IdentificationBillings201310.1002/9781118535561Billings S. A., (2013). Nonlinear System Identification : NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains. Chichester, UK:John Wiley & Sons.Intelligent Data Analysis VolBouroumi200010.3233/IDA-2000-43-406Unsupervised Fuzzy Learning and Cluster SeekingBouroumi A., Limouri M. and Essaïd A., (2000). Unsupervised Fuzzy Learning and Cluster Seeking. Intelligent Data Analysis, Vol. 4 No. 3.Int Journal of Control volChen49N03198910.1080/00207178908559683Representation of non-linear systems: the NARMAX modelChen S. and Billings S. A., (1989). Representation of non-linear systems: the NARMAX model. Int. Journal of Control, vol. 49, N03.Intelligent Systems (IS) 5th IEEE International ConferenceElayane201010.1109/is.2010.554840624h predictor of the Ozone process for Basse-Normandie region using fuzzy approachElayane E., Giri F., Pigeon E., and Massieu J-F., (2010). 24h predictor of the Ozone process for Basse-Normandie region using fuzzy approach. Intelligent Systems (IS), 5th IEEE International Conference.10.1002/9780470977811Everitt B.S., Landau S., Leese M. (2011), Cluster Analysis, 5th Edition. John Wiley & Sons, Ltd.10.1016/j.neuroscience.2020.12.001F. He and Y. Yang, "Nonlinear System Identification of Neural Systems from Neurophysiological Signals," Neuroscience, vol. 458, pp. 213-228, 2021, doi: 10.1016/j.neuroscience.2020.12.001.10.1109/TIA.2015.2416154F. Alonge, R. Rabbeni, M. Pucci, and G. Vitale, "Identification and Robust Control of a Quadratic DC/DC Boost Converter by Hammerstein Model," IEEE Trans. Ind. Appl., vol. 51, no. 5, pp. 3975-3985, 2015, doi: 10.1109/TIA.2015.2416154Pattern Recognit vol 43 no 4Falasconi1292201010.1016/j.patcog.2009.10.001Asability based validity method for fuzzy clusteringFalasconi M., Gutierrez A., Pardo M., Sberveglieri G., and Marco S., (2010). Asability based validity method for fuzzy clustering. Pattern Recognit. ,vol 43,no 4,pp 1292-1305.IEEE Transactions on knowledge and data engineering vol 19 n° 7François200710.1109/TKDE.2007.1037The Concentration of Fractional DistancesFrançois D., Wertz V., (2007). The Concentration of Fractional Distances. IEEE Transactions on knowledge and data engineering, vol 19, n° 7.Gasso K., (2000). Identification des systèmes dynamiques non-linéaires : approche multimodèle. Doctorat de l'institut National Polytechnique de Lorraine, Nancy.IEEE Transactions on Pattern Analysis and Machine Intelligence 7Gath773198910.1109/34.192473Unsupervised optimal fuzzy clusteringGath, I. and Geva A.B., (1989) .Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 773-781.Springer 1 editionGiri201010.1007/978-1-84996-513-2Block-oriented Nonlinear System IdentificationGiri F. and Bai E.-W., (2010). Block-oriented Nonlinear System Identification. Springer, 1 edition.Gustafson D.E. and Kessel V.C., (1979). Fuzzy clustering, with a fuzzy co-variance matrix. In: Proc IEEE. CDC, San Diego, 761-766.Proceedings of the IEEEKohonen199010.1109/5.58325The self-organizing mapKohonen T., (1990). The self-organizing map. Proceedings of the IEEE.Jakubek S., Keuth N., (2005). A New Training Algorithm for Neuro-Fuzzy Networks. In: Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics. Barcelona, Spain.ACM Computing SurveysJain199910.1145/331499.331504Data Clustering :A ReviewJain A.K., Murty M.N., Flynn, P.J., (1999). Data Clustering :A Review. ACM Computing Surveys.10.1016/0005-1098(94)00096-2Johansen T.A. and Foss A.B., ( 1995). Identification of non-linear system structure and parameters using regime decomposition. Automatica, 31.Applied Soft ComputingKroll25496201410.1016/j.asoc.2014.08.034Benchmark problems for nonlinear system identification and control using Soft Computing methods: Need and overviewKroll A., Schulte H., (2014). Benchmark problems for nonlinear system identification and control using Soft Computing methods: Need and overview. Applied Soft Computing 25, pp 496-51310.1016/j.automatica.2017.06.044Schoukens M. and Tiels K., "Identification of block-oriented nonlinear systems starting from linear approximations: A survey," Automatica, vol. 85, pp. 272-292, 2017, doi: 10.1016/j.automatica.2017.06.044.Control Engineering Practice vol 7Mourot707199910.1016/S0967-0661(99)00030-1Modelling of ozone concentrations using a Takagi-Sugeno modelMourot G., Gasso K., Ragot J., (1999). Modelling of ozone concentrations using a Takagi-Sugeno model. Control Engineering Practice, vol. 7, pp. 707-715.Murray-Smith R. et Johansen, T. A., (1997). Multiple model approaches to modeling and control. Taylor &Francis, London.10.1109/ISIC.2014.6967610Naitali A., Giri F., Radouane A., Chaoui F. Z (2014). Swarm intelligence based partitioning in local linear models identification. ISIC 2014: 843-848.10.1109/72.80202Narendra K., Parthasarathy K., (1990). Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Netw. Pp: 1 4-27.Proceeding of 11th IFAC Symposium on System IdentificationNelles199710.1016/s1474-6670(17)42917-xOrthonormal basis functions for nonlinear system identification with local linear model trees (LOLIMOT)Nelles O. (1997). Orthonormal basis functions for nonlinear system identification with local linear model trees (LOLIMOT). Proceeding of 11th IFAC Symposium on System Identification. Kitakyushu, Fukuoka, Japan.Springer-VerlagNelles200110.1007/978-3-662-04323-3Nonlinear system identificationNelles O., (2001). Nonlinear system identification. Springer-Verlag. Berlin HeidelbergAutomaticaPaduart464647201010.1016/j.automatica.2010.01.001Identification of nonlinear systems using polynomial nonlinear state space modelsPaduart, J., Lauwers, L., Swevers, J., Smolders, K., Schoukens, J., & Pintelon, R., (2010). Identification of nonlinear systems using polynomial nonlinear state space models. Automatica, 46(4), 647-656.10.1109/91.413225Pal N. R, Bezdek (1995). On cluster validity for th fuzzy c-means model. IEEE Trans. On Fuzzy Systems Vol. 3, no 3, pp. 370-37911th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing Vol 11Radouane605201310.3182/20130703-3-fr-4038.00064Similarity Improvement Using Angular Deviation in Multimodel Nonlinear System IdentificationRadouane A., Giri F., Naitali A., Chaoui FZ., (2013). Similarity Improvement Using Angular Deviation in Multimodel Nonlinear System Identification. 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing. Vol. 11, pp. 605-610.10.1080/03772063.2007.10876119Purwar S., Kar I N & Jha A N (2007) Nonlinear System Identification using Neural Networks, IETE Journal of Research, 53:1, 35-42,Environmental Modelling & Software 23Salazar-Ruiz200810.1016/j.envsoft.2007.11.009Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US)Salazar-Ruiz A., Ordieres J.B., Vergara E.P,. Capuz-Rizo S.F, (2008). Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US). Environmental Modelling & Software 23. Pp 1056-1069Shorten R. and Murray-Smith R., (1997). Multiple model approaches to modelling and control, chapter Side-effects of normalizing basis functions in local model networks. Taylor and Francis.Shyjan M., Martial H., (2003). The Optimal Distance Measure for Object Detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'03)10.1109/91.273127Sun C.T, (1994). Rule-base structure identification in an adaptive-network-based fuzzy inference system. IEEE Trans. on Fuzzy Systems, 2(1) pages:64-73.IEEE Transactions on Systems Man and Cybernetics 15 (1)Takagi116198510.1109/TSMC.1985.6313399Fuzzy identification of systems and its application to modelling and controlTakagi T.M. and Sugeno M., (1985). Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems, Man and Cybernetics 15 (1), 116-132.neural networks IEEE transactionsTeslic201110.1109/TNN.2011.2170093Nonlinear System Identification by Gustafson- Kessel Fuzzy Clustering and Supervised Local Model Network Learning for the Drug Absorption Spectra ProcessTeslic L., Hartmann B., Nelles O., and Škrjanc I., (2011). Nonlinear System Identification by Gustafson- Kessel Fuzzy Clustering and Supervised Local Model Network Learning for the Drug Absorption Spectra Process. neural networks, IEEE transactions.Trabelsi A., Lafont F., Kamoun M. and Enea G., (2004). Identification of nonlinear multivariable systems by adaptive fuzzy Takagi-Sugeno model. International Journal of Computational Cognition. Volume 2, Number 3, Pages 137-153.Fuzzy Sets and Systems Vol 5Windham177198110.1016/0165-0114(81)90015-4Cluster validity for fuzzy clustering algorithmWindham M.P., (1981). Cluster validity for fuzzy clustering algorithm. Fuzzy Sets and Systems. Vol. 5, pp. 177-185.10.1080/00207179.2019.1658134Xin Liu, Xianqiang Yang & Xiaofeng Liu (2019): Nonlinear state-space system identification with robust Laplace model, International Journal of Control, DOI:10.1080/00207179.2019.165813410.1080/002071797223028H. Gollee & K. J. Hunt (1997) Nonlinear modelling and control of electrically stimulated muscle: A local model network approach, International Journal of Control, 68:6, 1259-1288.Mahalanobis, P.C. (1936) On the Generalized Distance in Statistics. Proceedings of the National Institute of Science of India, 2, 49-55.