Identification of polynomial input/output recursive models with simulation error minimisation methods

References

• Aguirre , LA and Billings , SA . 1995a . Dynamical Effects of Overparametrization in Nonlinear Models . Physica D , 80 : 26 – 40 .
   Web of Science ®Google Scholar
• Aguirre , LA and Billings , SA . 1995b . Improved Structure Selection for Nonlinear Models based on Term Clustering . International Journal of Control , 62 : 569 – 587 .
   Web of Science ®Google Scholar
• Aguirre , LA , Coelho , MCS and Corrêa , MV . 2005 . On the Interpretation and Practice of Dynamical Differences between Hammerstein and Wiener Models . IEE Proceedings, Part D – Control Theory and Applications , 152 : 349 – 356 .
   Google Scholar
• Aguirre , LA , Corrêa , MV and Cassini , CCS . 2002 . Nonlinearities in NARX Polynomial Models: Representation and Estimation . IEE Proceedings, Part D – Control Theory and Applications , 149 : 343 – 348 .
   Google Scholar
• Aguirre , LA , Donoso-Garcia , PF and Santos-Filho , R . 2000 . Use of a Priori Information in the Identification of Global Nonlinear Models – A Case Study Using a Buck Converter . IEEE Transactions on Circuits and Systems, Part I: Fundamental Theory and Applications , 47 : 1081 – 1085 .
   Google Scholar
• Billings , SA and Aguirre , LA . 1995 . Effects of the Sampling Time on the Dynamics and Identification of Nonlinear Models . International Journal of Bifurcations and Chaos , 5 : 1541 – 1556 .
   Web of Science ®Google Scholar
• Billings , SA , Chen , S and Korenberg , MJ . 1989 . Identification of MIMO Non-linear Systems Using a Forward-regression Orthogonal Estimator . International Journal of Control , 49 : 2157 – 2189 .
   Web of Science ®Google Scholar
• Boaghe , OM , Billings , SA , Li , LM , Fleming , PJ and Liu , J . 2002 . Time and Frequency Domain Identification and Analysis of a Gas Turbine Engine . Control Engineering Practice , 10 : 1347 – 1356 .
   Web of Science ®Google Scholar
• Chiras , N , Evans , C and Rees , D . 2001 . Nonlinear Gas Turbine Modeling Using NARMAX Structures . IEEE Transactions on Instrumentation and Measurement , 50 : 893 – 898 .
   Web of Science ®Google Scholar
• Connally , P , Li , K and Irwin , GW . 2007 . Prediction- and Simulation-error Based Perceptron Training: Solution Space Analysis and a Novel Combined Training Scheme . Neurocomputing , 70 : 819 – 827 .
   Web of Science ®Google Scholar
• Farina, M., and Piroddi, L. (2008), ‘Some Convergence Properties of Multistep Prediction Error Identification Criteria’, in Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico, pp. 756–761.
   Google Scholar
• Farina, M., and Piroddi, L. (2009), ‘Simulation Error Minimization-based Identification of Polynomial Input-Output Recursive Models’, in Proceedings of the 15th IFAC Symposium on System Identification, Saint-Malo, France, pp. 1393–1398.
   Google Scholar
• Farina, M., and Piroddi, L. (2010), ‘An Iterative Algorithm for Simulation Error Based Identification of Polynomial Input-output Models Using Multi-step Prediction’, International Journal of Control, 83, 1442–1456.
   Google Scholar
• Haber , R and Unbehauen , H . 1990 . Structure Identification of Nonlinear Dynamic Systems – A Survey on Input/Output Approaches . Automatica , 26 : 651 – 677 .
   Web of Science ®Google Scholar
• Hong , X , Mitchell , RJ , Chen , S , Harris , CJ , Li , K and Irwin , GW . 2008 . Model Selection Approaches for Non-linear System Identification: A Review . International Journal of Systems Science , 39 : 925 – 946 .
   Web of Science ®Google Scholar
• Korenberg , M , Billings , SA , Liu , YP and McIlroy , PJ . 1988 . Orthogonal Parameter Estimation Algorithm for Non-linear Stochastic Systems . International Journal of Control , 48 : 193 – 210 .
   Web of Science ®Google Scholar
• Leontaritis , IJ and Billings , SA . 1985a . Input-Output Parametric Models for Non-linear Systems – Part I: Deterministic Non-linear Systems . International Journal of Control , 41 : 303 – 328 .
   Web of Science ®Google Scholar
• Leontaritis , IJ and Billings , SA . 1985b . Input-Output Parametric Models for Non-linear Systems – Part II: Stochastic Non-linear Systems . International Journal of Control , 41 : 329 – 344 .
   Web of Science ®Google Scholar
• Leva , A and Piroddi , L . 2002 . NARX-Based Technique for the Modeling of Magneto-rheological Damping Devices . Smart Materials and Structures , 11 : 79 – 88 .
   Web of Science ®Google Scholar
• Li , K , Peng , J-X and Bai , E-W . 2009 . Two-stage Mixed Discrete-continuous Identification of Radial Basis Function (RBF) Neural Models for Nonlinear Systems . IEEE Transactions on Circuits and Systems:I. Regular Papers , 56 : 630 – 643 .
   Google Scholar
• Li , K , Peng , J and Irwin , G . 2005 . A Fast Nonlinear Model Identification Method . IEEE Transactions on Automatic Control , 50 : 1211 – 1216 .
   Web of Science ®Google Scholar
• Loh , CH and Duh , JY . 1996 . Analysis of Nonlinear System using NARMA Models . JSCE – Structural Engineering/Earthquake Engineering , 13 : 11 – 21 .
   Google Scholar
• Maio, I.A., Stievano, I.S., and Canavero, F.G. (1998), ‘NARX Approach to Black-box Modeling of Circuit Elements’, in Proceedings of the IEEE International Symposium on Circuits and Systems, ISCAS 98, Monterey Conference Center, Monterey (CA), USA, pp. 411–414.
   Google Scholar
• Palumbo , P and Piroddi , L . 2000 . Seismic Behaviour of Buttress Dams: Nonlinear Modelling of a Damaged Buttress Based on ARX/NARX Models . Journal of Sound and Vibration , 239 : 405 – 422 .
   Web of Science ®Google Scholar
• Peng, J.X., Li, K., and Bai, E.W. (2006a), ‘A Two-stage Algorithm for Identification of Nonlinear Dynamic Systems’, in Proceedings of the 14th IFAC Symposium on System Identification, Newcastle, Australia, pp. 570–575.
   Google Scholar
• Peng , J-X , Li , K and Huang , DS . 2006b . A Hybrid Forward Algorithm for RBF Neural Network Construction . IEEE Transactions on Neural Networks , 17 : 1439 – 1451 .
   PubMed Web of Science ®Google Scholar
• Peyton-Jones , JC and Billings , SA . 1989 . Recursive Algorithm for Computing the Frequency Response of a Class of Non-linear Difference Equation Models . International Journal of Control , 50 : 1925 – 1940 .
   Web of Science ®Google Scholar
• Piroddi , L . 2008 . Simulation Error Minimization Methods for NARX Model Identification . International Journal of Modeling, Identification and Control , 3 : 392 – 403 .
   Google Scholar
• Piroddi, L., Farina, M., and Lovera, M. (2009), ‘Polynomial NARX Model Identification: A Wiener–Hammerstein Benchmark’, in Proceedings of the 15th IFAC Symposium on System Identification, Saint-Malo, France, pp. 1074–1079.
   Google Scholar
• Piroddi , L and Spinelli , W . 2003 . An Identification Algorithm for Polynomial NARX Models based on Simulation Error Minimization . International Journal of Control , 76 : 1767 – 1781 .
   Web of Science ®Google Scholar
• Pottmann , M , Unbehauen , H and Seborg , DE . 1993 . Application of a General Multi-model Approach for Identification of Highly Nonlinear Processes – A Case Study . International Journal of Control , 57 : 97 – 120 .
   Web of Science ®Google Scholar
• Schoukens, J., Suykens, J., and Ljung, L. (2008), ‘Wiener–Hammerstein Benchmark’, Invited Session Proposal for the 15th IFAC Symposium on System Identification, Saint-Malo, France.
   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 , 12 : 1691 – 1724 .
   Web of Science ®Google Scholar
• Söderström , T and Stoica , P . 1989 . System Identification , London : Prentice-Hall .
   Google Scholar
• Spinelli, W., Piroddi, L., and Li, K. (2004), ‘Nonlinear Modeling of NOX Emission in a Coal-fired Power Generation Plant’, in Proceedings of the 43rd IEEE Conference on Decision and Control, Atlantis, Paradise Island, Bahamas, pp. 3850–3855.
   Google Scholar
• Vandersteen, G. (1997), ‘Identification of Linear and Nonlinear Systems in an Errors–in–variables Least Squares and Total Least Squares Framework’, Ph.D thesis, Vrije Universiteit Brussel, Belgium.
   Google Scholar