An Introduction To Models Based On Laguerre, Kautz And Other Related Orthonormal Functions - Part Ii: Non-linear Models

dc.creatorOliveira G.H.C.
dc.creatorDa Rosa A.
dc.creatorCampello R.J.G.B.
dc.creatorMacHado J.B.
dc.creatorAmaral W.C.
dc.date2012
dc.date2015-06-25T20:24:19Z
dc.date2015-11-26T15:19:58Z
dc.date2015-06-25T20:24:19Z
dc.date2015-11-26T15:19:58Z
dc.date.accessioned2018-03-28T22:29:28Z
dc.date.available2018-03-28T22:29:28Z
dc.identifier
dc.identifierInternational Journal Of Modelling, Identification And Control. , v. 16, n. 1, p. 1 - 14, 2012.
dc.identifier17466172
dc.identifier10.1504/IJMIC.2012.046691
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84860779184&partnerID=40&md5=35df6e7590a5c9dc3171fa7fded04c45
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/90198
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/90198
dc.identifier2-s2.0-84860779184
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1259862
dc.descriptionThis paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalised orthonormal basis functions. The paper is separated in two parts. The first part of the paper approached issues related with linear models and models with uncertain parameters. Now, the mathematical foundations as well as their advantages and limitations are discussed within the contexts of non-linear system identification. The discussions comprise a broad bibliographical survey of the subject and a comparative analysis involving some specific model realisations, namely, Volterra, fuzzy, and neural models within the orthonormal basis functions framework. Theoretical and practical issues regarding the identification of these non-linear models are presented and illustrated by means of two case studies. Copyright © 2012 Inderscience Enterprises Ltd.
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dc.description1
dc.description1
dc.description14
dc.descriptionAlataris, K., Berger, T.W., Marmarelis, V.Z., A novel network for nonlinear modeling of neural systems with arbitrary point-process inputs (2000) Neural Networks, 13 (2), pp. 255-266. , DOI 10.1016/S0893-6080(99)00092-1, PII S0893608099000921
dc.descriptionArto, V., Hannu, P., Halme, A., Modeling of chromatographic separation process with Wiener-MLP representation (2001) Journal of Process Control, 11 (5), pp. 443-458. , DOI 10.1016/S0959-1524(00)00053-6, PII S0959152400000536
dc.descriptionBabuška, R., (1998) Fuzzy Modeling for Control, , Kluwer Academic Publishers, Massachusetts, USA
dc.descriptionBabuška, R., Verbruggen, H.B., Fuzzy set methods for local modelling and identification (1997) Multiple Model Approaches to Modelling and Control, , Murray-Smith, R. and Johansen, T.A. (Eds.) Taylor and Francis, London, Chap. 2
dc.descriptionBack, A.D., Tsoi, A.C., Nonlinear system identification using discrete Laguerre functions (1996) Journal of Systems Engineering, 6 (3), pp. 194-207
dc.descriptionBalestrino, A., Caiti, A., Zanobini, G., Identification of Wiener-type nonlinear systems by Laguerre filters and neural networks (1999) Proc. 14th IFAC World Congress, pp. 433-438. , Beijing/China
dc.descriptionBillings, S.A., Identification of nonlinear systems - A survey (1980) IEE Proc. Pt D, 127 (6), pp. 272-285
dc.descriptionBoyd, S., Chua Leon, O., Fading memory and the problem of approximating nonlinear operators with volterra series (1985) IEEE transactions on circuits and systems, CAS-32 (11), pp. 1150-1161
dc.descriptionBroome, P.W., Discrete orthonormal sequences (1965) Journal of the Association for Computing Machinery, 12 (2), pp. 151-168
dc.descriptionBroomhead, D.S., Lowe, D., Multivariate functional interpolation and adaptive networks (1988) Complex Systems, 2 (3), pp. 321-355
dc.descriptionCampello, R.J.G.B., (2002) New Architectures and Methodologies for Modeling and Control of Complex Systems Combining Classical and Modern Tools, , PhD thesis, School of Electrical and Computer Engineering of the State University of Campinas (FEEC/UNICAMP), Campinas-SP, Brazil (in Portuguese)
dc.descriptionCampello, R.J.G.B., Amaral, W.C., Takagi-Sugeno fuzzy models within orthonormal basis function framework and their application to process control (2002) IEEE International Conference on Plasma Science, 2, pp. 1399-1404
dc.descriptionCampello, R.J.G.B., Oliveira, G.H.C., Modelos não lineares (2007) Enciclopédia de Automática, 3. , L.A. Aguirre, A.P. Alves da Silva, M.F.M. Campos and W.C. Amaral (Eds.) (Cap. 4), Edgard Blücher (in Portuguese)
dc.descriptionCampello, R.J.G.B., Meleiro, L.A.C., Amaral, W.C., Control of a bioprocess using orthonormal basis function fuzzy models (2004) IEEE International Conference on Fuzzy Systems, 2, pp. 801-806. , 2004 IEEE International Conference on Fuzzy Systems - Proceedings
dc.descriptionCampello, R.J.G.B., Von Zuben, F.J., Amaral, W.C., Meleiro, L.A.C., Maciel Filho, R., Hierarchical fuzzy models within the framework of orthonormal basis functions and their application to bioprocess control (2003) Chemical Engineering Science, 58 (18), pp. 4259-4270. , DOI 10.1016/S0009-2509(03)00309-9
dc.descriptionChen, S., Billings, S., Grant, P., Recursive hybrid algorithm for non-linear system identification using radial basis function networks (1992) Int. J. Control, 55 (5), pp. 1051-1070
dc.descriptionCho, K.B., Wang, B.H., Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction (1996) Fuzzy Sets and Systems, 83 (3), pp. 325-339
dc.descriptionDa Rosa, A., (2005) Expansion of discrete-time Volterra models using Kautz functions, , MSc thesis, School of Electrical and Computer Engineering of the State University of Campinas (FEEC/UNICAMP), Campinas- SP, Brazil (in Portuguese)
dc.descriptionDa Rosa, A., Campello, R.J.G.B., Amaral, W.C., An optimal expansion of Volterra models using independent Kautz bases for each kernel dimension (2008) International Journal of Control, 81 (6), pp. 962-975
dc.descriptionDa Rosa, A., Campello, R.J.G.B., Amaral, W.C., Exact search directions for optimization of linear and nonlinear models based on generalized orthonormal functions (2009) IEEE Transactions on Automatic Control, 54 (12), pp. 2757-2772
dc.descriptionDoyle Iii, F.J., Ogunnaike, B.A., Pearson, R.K., Nonlinear model-based control using second-order Volterra models (1995) Automatica, 31 (5), pp. 697-714
dc.descriptionDoyle Iii, F.J., Pearson, R.K., Ogunnaike, B.A., (2002) Identification and Control Using Volterra Models, , Springer-Verlag, London, UK
dc.descriptionDumont, G.A., Fu, Y., Non-linear adaptive control via Laguerre expansion of Volterra kernels (1993) Int. J. Adaptive Control and Signal Processing, 7 (5), pp. 367-382
dc.description(1999) Manual for Model 730 - Magnetic Levitation System, , Educational Control Products (ECP), California, USA
dc.descriptionEspinosa, J., Vandewalle, J., Wertz, V., (2004) Fuzzy Logic, Identification and Predictive Control, , Springer-Verlag, London, UK
dc.descriptionEykhoff, P., (1974) System Identification: Parameter and State Estimation, , John Wiley & Sons, UK
dc.descriptionFu, Y., Dumont, G.A., Optimum time scale for discrete Laguerre network (1993) IEEE Transactions on Automatic Control, 38 (6), pp. 934-938. , DOI 10.1109/9.222305
dc.descriptionGustafson, D.E., Kessel, W.C., Fuzzy clustering with a fuzzy covariance matrix (1979) Proc. IEEE CDC, pp. 761-766. , San Diego, CA
dc.descriptionHaykin, S., (1999) Neural Networks: A Comprehensive Foundation, , 2nd ed., Prentice Hall, Upper Saddle River, NJ, USA
dc.descriptionHeuberger, P.S.C., Van Den Hof, P.M.J., Bosgra, O.H., A generalized orthonormal basis for linear dynamical systems (1995) IEEE Trans. on Automatic Control, 40 (3), pp. 451-465
dc.descriptionHeuberger, P.S.C., Van Den Hof, P.M.J., Wahlberg, B., (2005) Modelling and Identification with Rational Orthogonal Basis Functions, , Springer-Verlag, London, UK
dc.descriptionHunt, K.J., Haas, R., Murray-Smith, R., Extending the functional equivalence of radial basis function networks and fuzzy inference systems (1996) IEEE Transactions on Neural Networks, 7 (3), pp. 776-781. , PII S1045922796012398
dc.descriptionKosko, B., (1992) Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, , Prentice Hall, Upper Saddle River, NJ, USA
dc.descriptionKosko, B., (1997) Fuzzy Engineering, , Prentice Hall, Upper Saddle River, NJ, USA
dc.descriptionLeontaritis, I., Billings, S.A., Input-output parametric models for nonlinear systems - Parts i and II (1985) Int. Journal of Control, 41 (6), pp. 303-344
dc.descriptionLjung, L., (1999) System Identification: Theory for the User, , 2nd ed., Prentice Hall, Upper Saddle River, NJ, USA
dc.descriptionMacHado, J.B., Design of OBF-TS fuzzy models based on multiple clustering validity criteria (2007) Proc. IEEE Int. Conf. on Tools with Artificial Intelligence, (2), pp. 336-339. , Patras/Greece
dc.descriptionMäkilä, P.M., Approximation of stable systems by Laguerre filters (1990) Automatica, 26 (2), pp. 333-345
dc.descriptionManer, B.R., Doyle III, F.J., Ogunnaike, B.A., Pearson, R.K., Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models (1996) Automatica, 32 (9), pp. 1285-1301. , DOI 10.1016/0005-1098(96)00086-6, PII S0005109896000866
dc.descriptionMedeiros, A.V., Amaral, W.C., Campello, R.J.G.B., GA optimization of generalized OBF-TS fuzzy models with global and local estimation approaches (2006) Proc. 15th IEEE Internat. Conference on Fuzzy Systems, pp. 8494-8501. , Vancouver/Canada
dc.descriptionNarendra, K., Parthasarathy, K., Identification and control of dynamical systems using neural networks (1990) IEEE Trans. Neural Networks, 1 (1), pp. 4-26
dc.descriptionNelles, O., (2001) Nonlinear System Identification, , Springer-Verlag, London, UK
dc.descriptionNinness, B., Gustafsson, F., A unifying construction of orthonormal bases for system identification (1997) IEEE Transactions on Automatic Control, 42 (4), pp. 515-521. , PII S0018928697028080
dc.descriptionOliveira, G.H.C., Amaral, W.C., Latawiec, K., CRHPC using Volterra models and orthonormal basis functions: An application to CSTR plants (2003) Proc. IEEE Conference on Control Applications, pp. 718-723. , Istanbul/Turkey
dc.descriptionOliveira, G.H.C., Campello, R.J.G.B., Amaral, W.C., Fuzzy models within orthonormal basis function framework (1999) Proc. 8th IEEE Internat. Conference on Fuzzy Systems, pp. 957-962. , Seoul/Korea
dc.descriptionOliveira, G.H.C., Da Rosa, A., Campello, R.J.G.B., MacHado, J.M., Amaral, W.C., An introduction to models based on Laguerre, Kautz and other related orthonormal functions - Part I: Linear and uncertain models (2011) International Journal of Modelling, Identification and Control, 14 (1-2)
dc.descriptionPassino, K.M., Yurkovich, S., (1997) Fuzzy Control, , Addison-Wesley Longman Inc., USA
dc.descriptionPearson, R.K., (1999) Discrete-Time Dynamic Models, , Oxford University Press, New York, USA
dc.descriptionPottmann, M., Seborg, D., Identification of non-linear process using reciprocal multiquadric functions (1992) Journal of Process Control, 2 (4), pp. 189-203
dc.descriptionRugh, W.J., (1981) Nonlinear System Theory: The Volterra/Wiener Approach, , The Johns Hopkins University Press, Baltimore, USA
dc.descriptionRumelhart, D., McClelland, J., (1986) Parallel Distributed Processing, 1. , PDP Research Group MIT Press, Cambridge, MA, USA
dc.descriptionSaraswati, S., Chand, S., Neural network models for multi-step ahead prediction of air-fuel ratio in SI engines (2009) International Journal of Modelling, Identification and Control, 7 (3), pp. 263-274
dc.descriptionSchetzen, M., (1980) The Volterra and Wiener Theories of Nonlinear Systems, , Krieger Publishing Company, Malabar, Florida, USA
dc.descriptionSentoni, G., Agamennoni, O., Desages, A., Romagnoli, J., Approximate models for nonlinear process control (1996) AIChE Journal, 42 (8), pp. 2240-2250
dc.descriptionSentoni, G.B., Biegler, L.T., Guiver, J.B., Zhao, H., State-space nonlinear process modeling: Identification and universality (1998) AIChE Journal, 44 (10), pp. 2229-2239
dc.descriptionSjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.-Y., Hjalmarsson, H., Juditsky, A., Nonlinear black-box modeling in system identification: A unified overview (1995) Automatica, 31 (12), pp. 1691-1724
dc.descriptionSu, H.-T., McAvoy, T., Integration of multilayer perceptron networks and linear dynamic models: A Hammerstein modeling approach (1993) Ind. Eng. Chem. Res., 32 (9), pp. 1927-1936
dc.descriptionSugeno, M., Kang, G.T., Fuzzy modelling and control of multilayer incinerator (1986) Fuzzy Sets and Systems, 18 (3), pp. 329-346
dc.descriptionSugeno, M., Kang, G.T., Structure identification of fuzzy model (1988) Fuzzy Sets and Systems, 28 (1), pp. 15-33
dc.descriptionSugeno, M., Tanaka, K., Successive identification of a fuzzy model and its applications to prediction of a complex system (1991) Fuzzy Sets and Systems, 42 (3), pp. 315-334
dc.descriptionTakagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Trans. Systems, Man and Cybernetics, SMC-15, pp. 116-132
dc.descriptionTakenaka, S., On the orthogonal functions and a new formula of interpolation (1925) Japanese Journal of Mathematics, 2, pp. 129-145
dc.descriptionVan Den Hof, P.M.J., Heuberger, P.S.C., Bokor, J., System identification with generalized orthonormal basis functions (1995) Automatica, 31 (12), pp. 1821-1834
dc.descriptionAbrahantes Vazquez, M.A., Agamennoni, O.E., Approximate models for nonlinear dynamical systems and their generalization properties (2001) Mathematical and Computer Modelling, 33 (8-9), pp. 965-986. , DOI 10.1016/S0895-7177(00)00293-4, PII S0895717700002934
dc.descriptionWang, L.-X., Mendel, J.M., Fuzzy basis functions, universal approximation and orthogonal least squares learning (1992) IEEE Trans. Neural Networks, 3 (5), pp. 807-814
dc.descriptionWiener, N., (1958) Nonlinear Problems in Random Theory, , MIT Press, Cambridge, MA, USA
dc.descriptionYager, R.R., Filev, D.P., (1994) Essentials of Fuzzy Modeling and Control, , John Wiley & Sons, USA
dc.descriptionZeng, X.-J., Singh, M.G., Approximation theory of fuzzy systems - SISO case (1994) IEEE Trans. Fuzzy Systems, 2 (2), pp. 162-176
dc.descriptionZeng, X.-J., Singh, M.G., Approximation theory of fuzzy systems - MIMO case (1995) IEEE Trans. Fuzzy Systems, 3 (2), pp. 219-235
dc.descriptionZiaei, K., Wang, D.W.L., Application of orthonormal basis functions for identification of flexible-link manipulators (2006) Control Engineering Practice, 14 (2), pp. 99-106. , DOI 10.1016/j.conengprac.2004.11.020, PII S0967066105000365
dc.languageen
dc.publisher
dc.relationInternational Journal of Modelling, Identification and Control
dc.rightsfechado
dc.sourceScopus
dc.titleAn Introduction To Models Based On Laguerre, Kautz And Other Related Orthonormal Functions - Part Ii: Non-linear Models
dc.typeArtículos de revistas


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