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Relevant bibliographies by topics / Recursive identification

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Academic literature on the topic 'Recursive identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Recursive identification"

1

Park, H. M., and W. J. Lee. "Recursive Identification of Thermal Convection." Journal of Dynamic Systems, Measurement, and Control 125, no. 1 (2003): 1–10. http://dx.doi.org/10.1115/1.1540116.

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A method is developed for the recursive identification of thermal convection system governed by the Boussinesq equation using an extended Kalman filter. A computationally feasible Kalman filter is constructed by reducing the Boussinesq equation to a small number of ordinary differential equations by means of the Karhunen-Loe`ve Galerkin procedure which is a type of Galerkin method employing the empirical eigenfunctions of the Karhunen-Loe`ve decomposition. Employing the Kalman filter constructed by using the reduced order model, the thermal convection induced by a spatially varying heat flux at the bottom is identified recursively by using either the Boussinesq equation or the reduced order model itself. The recursive identification technique developed in the present work is found to yield accurate results for thermal convection even with approximate covariance equation and noisy measurements. It is also shown that a reasonably accurate and computationally feasible method of recursive identification can be constructed even with a relatively inaccurate reduced order model.

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Chen, Han-Fu. "Recursive system identification." Acta Mathematica Scientia 29, no. 3 (2009): 650–72. http://dx.doi.org/10.1016/s0252-9602(09)60062-x.

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El-Sheikh, Garnal. "Recursive Identification Methods." International Conference on Aerospace Sciences and Aviation Technology 7, ASAT CONFERENCE (1997): 1–9. http://dx.doi.org/10.21608/asat.1997.25437.

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Ljung, Lennart. "Recursive identification algorithms." Circuits, Systems, and Signal Processing 21, no. 1 (2002): 57–68. http://dx.doi.org/10.1007/bf01211651.

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Wani Jamaludin, Irma Wani Jamaludin, and Norhaliza Abdul Wahab. "Recursive Subspace Identification Algorithm using the Propagator Based Method." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 1 (2017): 172. http://dx.doi.org/10.11591/ijeecs.v6.i1.pp172-179.

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<p>Subspace model identification (SMI) method is the effective method in identifying dynamic state space linear multivariable systems and it can be obtained directly from the input and output data. Basically, subspace identifications are based on algorithms from numerical algebras which are the QR decomposition and Singular Value Decomposition (SVD). In industrial applications, it is essential to have online recursive subspace algorithms for model identification where the parameters can vary in time. However, because of the SVD computational complexity that involved in the algorithm, the classical SMI algorithms are not suitable for online application. Hence, it is essential to discover the alternative algorithms in order to apply the concept of subspace identification recursively. In this paper, the recursive subspace identification algorithm based on the propagator method which avoids the SVD computation is proposed. The output from Numerical Subspace State Space System Identification (N4SID) and Multivariable Output Error State Space (MOESP) methods are also included in this paper.</p>

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Yan, Zheping, Di Wu, Jiajia Zhou, and Lichao Hao. "Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/547539.

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A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs) is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV) one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM) based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.

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Feng, Xu, Ching-Fang Lin, and Norman P. Coleman. "Frequency-Domain Recursive Robust Identification." Journal of Guidance, Control, and Dynamics 23, no. 5 (2000): 908–10. http://dx.doi.org/10.2514/2.4628.

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Le, Fengmin, Ivan Markovsky, Christopher Freeman, and Eric Rogers. "Recursive Identification of Hammerstein Systems." IFAC Proceedings Volumes 44, no. 1 (2011): 13954–59. http://dx.doi.org/10.3182/20110828-6-it-1002.00313.

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Unton, F. Z. "A New Recursive Identification Technique." IFAC Proceedings Volumes 18, no. 5 (1985): 873–78. http://dx.doi.org/10.1016/s1474-6670(17)60671-2.

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Sanchis, R., and P. Albertos. "Recursive Identification under Scarce Measurements." IFAC Proceedings Volumes 33, no. 15 (2000): 745–50. http://dx.doi.org/10.1016/s1474-6670(17)39841-5.

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Dissertations / Theses on the topic "Recursive identification"

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Chow, Po-Chuan. "Recursive nonlinear identification of Hammerstein-type systems." Case Western Reserve University School of Graduate Studies / OhioLINK, 1990. http://rave.ohiolink.edu/etdc/view?acc_num=case1054758543.

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Oliveira, Tiago Miguel Brites. "Recursive neuro fuzzy techniques for online identification and control." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10552.

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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores<br>The main goal of this thesis will be focused on developing an adaptative closed loop control solution, using fuzzy methodologies. A positive theoretical and experimental contribution, regarding modelling and control of fuzzy and neuro fuzzy systems, is expected to be achieved. Proposed non-linear identification solution will use for modelling and control, a recurrent neuro fuzzy architecture. Regarding model solution, a state space approach will be considered during fuzzy consequent local models design. Developed controller will be based on model parameters, being expected not only a stable closed loop solution, but also a static error with convergence towards zero. Model and controller fuzzy subspaces, will be partitioned throughout process dynamical universe, allowing fuzzy local models and controllers commutation and aggregation. With the aim of capturing process under control dynamics using a real time approach, the use of recursive optimization techniques are to be adopted. Such methods will be applied during parameter and state estimation, using a dual decoupled Kalman filter extended with unscented transformation. Two distinct processes one single-input (SISO) other multi-input (MIMO), will be used during experimentation. It is expected from experiments, a practical validation of proposed solution capabilities for control and identification. Presented work will not be completed, without first presenting a global analysis of adopted concepts and methods, describing new perspectives for future investigations.

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Brus, Linda. "Recursive black-box identification of nonlinear state-space ODE models." Licentiate thesis, Uppsala : Department of Information Technology, Uppsala University, 2006. http://www.it.uu.se/research/publications/lic/2006-001/.

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Ng, C. N. "Recursive identification, estimation and forecasting of non-stationary time series." Thesis, Lancaster University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383583.

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Pollard, Stephen J. "Recursive parameter identification for estimating and displaying maneuvering vessel path." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FPollard.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2003.<br>Thesis advisor(s): Roberto Cristi, Fotis A. Papoulias. Includes bibliographical references (p. 155). Also available online.

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Dai, Liang. "Identification using Convexification and Recursion." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-280422.

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System identification studies how to construct mathematical models for dynamical systems from the input and output data, which finds applications in many scenarios, such as predicting future output of the system or building model based controllers for regulating the output the system. Among many other methods, convex optimization is becoming an increasingly useful tool for solving system identification problems. The reason is that many identification problems can be formulated as, or transformed into convex optimization problems. This transformation is commonly referred to as the convexification technique. The first theme of the thesis is to understand the efficacy of the convexification idea by examining two specific examples. We first establish that a l1 norm based approach can indeed help in exploiting the sparsity information of the underlying parameter vector under certain persistent excitation assumptions. After that, we analyze how the nuclear norm minimization heuristic performs on a low-rank Hankel matrix completion problem. The underlying key is to construct the dual certificate based on the structure information that is available in the problem setting. Recursive algorithms are ubiquitous in system identification. The second theme of the thesis is the study of some existing recursive algorithms, by establishing new connections, giving new insights or interpretations to them. We first establish a connection between a basic property of the convolution operator and the score function estimation. Based on this relationship, we show how certain recursive Bayesian algorithms can be exploited to estimate the score function for systems with intractable transition densities. We also provide a new derivation and interpretation of the recursive direct weight optimization method, by exploiting certain structural information that is present in the algorithm. Finally, we study how an improved randomization strategy can be found for the randomized Kaczmarz algorithm, and how the convergence rate of the classical Kaczmarz algorithm can be studied by the stability analysis of a related time varying linear dynamical system.

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Schaffer, Scott E. (Scott Erwin). "The effect of a gap nonlinearity on recursive parameter identification algorithms." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/34039.

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Linden, J. G. "Algorithms for recursive Frisch scheme identification and errors-in-variables filtering." Thesis, Coventry University, 2008. http://curve.coventry.ac.uk/open/items/a9c4fe1c-08ba-b76f-4882-33b909a83abd/1.

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This thesis deals with the development of algorithms for recursive estimation within the errors-in-variables framework. Within this context attention is focused on two major threads of research: Recursive system identification based on the Frisch scheme and the extension and application of errors-in-variables Kalman filtering techniques. In the first thread, recursive algorithms for the approximate update of the estimates obtained via the Frisch scheme, which makes use of the Yule-Walker model selection criterion, are developed for the case of white measurement noise. Gradient-based techniques are utilised to update the Frisch scheme equations, which involve the minimisation of the model selection criterion as well as the solution of an eigenvalue problem, in a recursive manner. The computational complexity of the resulting algorithms is critically analysed and, by introducing additional approximations, fast recursive Frisch scheme algorithms are developed, which reduce the computational complexity from cubic to quadratic order. In addition, it is investigated how the singularity condition within the Frisch scheme is affected when the estimates are computed recursively. Whilst this first group of recursive Frisch scheme algorithms is developed directly from the offline Frisch scheme equations, it is also possible to interpret the Frisch scheme within an extended bias compensating least squares framework. Consequently, the development of recursive algorithms, which update the estimate obtained from the extended bias compensated least squares technique, is considered. These algorithms make use of the bilinear parametrisation principle or, alternatively, the variable projection method. Finally, two recursive Frisch scheme algorithms are developed for the case of coloured output noise. The second thread, which considers the theory of errors-in-variables filtering for linear systems, extends the approach to deal with a class of bilinear systems, a frequently used subset of nonlinear systems. The application of errors-in-variables filtering for the purpose of system identification is also considered. This leads to the development of a prediction error method based on symmetric innovations, which resembles the joint output method. Both the offline and online implementation of this novel identification technique are investigated.

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MBOUD, MAMADOU. "Identification adaptative par structures predictive et recursive : application a l'annulation d'echo acoustique." Paris 11, 1992. http://www.theses.fr/1992PA112448.

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Cette etude se place dans le contexte de l'identification adaptative, en temps reel, de reponse impulsionnelle longue, comme c'est le cas en annulation d'echo acoustique. Dans la premiere partie, le systeme a identifier est modelise par un filtre transverse. Une amelioration de la vitesse de convergence de l'algorithme lms est obtenue par une decorrelation du signal d'entree, a l'aide d'une prediction adaptative. Deux structures de pre-blanchiment sont issues de ce procede. Leur originalite repose sur la preservation des relations entree-sortie du systeme. Dans la deuxieme partie, le systeme a identifier est modelise par un filtre recursif, en vue d'une reduction de complexite. La theorie des approximations rationnelles permet de montrer la pertinence de tels modeles pour l'annulation d'echo acoustique. Afin de mieux comprendre le fonctionnement des algorithmes adaptatifs associes a ces modeles, les points stationnaires de trois algorithmes de filtrage adaptatif rii sont etudies. Encore une fois, la theorie des approximations rationnelles permet de donner, en sous-modelisation, la structure analytique de la fonction d'erreur correspondant a chaque point stationnaire

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Merchant, Richard W. "Recursive estimation using the bilinear operator with applications to synchronous machine parameter identification /." Title page, contents and abstract only, 1992. http://web4.library.adelaide.edu.au/theses/09PH/09phm5543.pdf.

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Books on the topic "Recursive identification"

1

Roy, Richard. Real-time flutter identification. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

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Postma, Steven Marten. Recursive implementation of a robust identification algorithm. National Library of Canada, 1994.

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Recursive identification based on the nonlinear Wiener model. Academia Upsaliensis, 1990.

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Juang, Jer-Nan. Recursive deadbeat controller design. National Aeronautics and Space Administration, Langley Research Center, 1997.

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5

Hanzon, Bernard. Identifiability, recursive identification and spaces of linear dynamical systems. Stichting Mathematische Centrum, 1989.

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Zhou, Quan-Gen. Recursive identification of time-varying systems: A new approach. National Library of Canada = Bibliothèque nationale du Canada, 1993.

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Hanzon, B. Identifiability, recursive identification and spaces of linear dynamical systems. Centre for Mathematics and Computer Science, 1989.

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Recursive nonlinear estimation: A geometric approach. Springer, 1996.

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Chen, S. Recursive maximum likelihood identification of a nonlinear output-affine model. University of Sheffield, Dept. of Control Engineering, 1987.

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10

Sundararajan, N. Adaptive identification and control of structural dynamics using recursive lattice filters. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

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Book chapters on the topic "Recursive identification"

1

Liu, G. P. "Recursive Nonlinear Identification." In Nonlinear Identification and Control. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0345-5_3.

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Walter, É., and H. Piet-Lahanier. "Recursive Robust Minimax Estimation." In Bounding Approaches to System Identification. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9545-5_12.

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Jiang, Z. H., and W. Schaufelberger. "Recursive block pulse function method." In Identification of Continuous-Time Systems. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3558-0_7.

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Lauer, Fabien, and Gérard Bloch. "Recursive and State-Space Identification of Hybrid Systems." In Hybrid System Identification. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00193-3_8.

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Chen, Han-Fu. "Recursive Identification for Stochastic Hammerstein Systems." In Lecture Notes in Control and Information Sciences. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-513-2_6.

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Verde, Cristina, and Jorge Rojas. "Recursive Scheme for Sequential Leaks’ Identification." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55944-5_7.

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Young, Peter C. "Identification of Transfer Function Models in Closed-Loop." In Recursive Estimation and Time-Series Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21981-8_9.

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Landau, I. D., R. Lozano, and M. M’Saad. "Recursive Plant Model Identification in Open Loop." In Adaptive Control. Springer London, 1998. http://dx.doi.org/10.1007/978-0-85729-343-5_5.

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Landau, I. D., R. Lozano, and M. M’Saad. "Recursive Plant Model Identification in Closed Loop." In Adaptive Control. Springer London, 1998. http://dx.doi.org/10.1007/978-0-85729-343-5_9.

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Landau, Ioan Doré, Rogelio Lozano, Mohammed M’Saad, and Alireza Karimi. "Recursive Plant Model Identification in Open Loop." In Adaptive Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-664-1_5.

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Conference papers on the topic "Recursive identification"

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Kim, Young-Man. "Threshold Selector for Fault Detection on Closed-Loop Predictor-Based Recursive System Identification." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-36157.

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In this research, predictor-based system identification is recursively implemented to identify system in closed-loop, which has been connected with proper threshold setting for fault detection. The predictor-based system identification technique is based on the assumption that the process and measurement noise is white. This paper shows how recursive system identification is connected with fault detection, which can remove the influence of noise. The effectiveness is demonstrated for recursive system identification combined with proper threshold setting via Matlab simulation.

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Dovžan, D., and I. Škrjanc. "Recursive Fuzzy Model Identification." In Advances in Computer Science and Engineering. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.689-027.

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Bilik, I., and J. Tabrikian. "Optimal recursive filtering using gaussian mixture model." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628628.

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Mattsson, Per, and Torbjorn Wigren. "Recursive identification of Hammerstein models." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859180.

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Chen Hanfu. "Recursive identification for ARMAX systems." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4604959.

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Feng, Xu, Tie-Jun Yu, Ching-Fang Lin, et al. "Recursive robust identification in frequency-domain." In Guidance, Navigation, and Control Conference. American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-3741.

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Bras, R., and J. M. Lemos. "Recursive sparse identification for adaptive control." In 2017 25th Mediterranean Conference on Control and Automation (MED). IEEE, 2017. http://dx.doi.org/10.1109/med.2017.7984238.

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Bathelt, Andreas, Dirk Soffker, and Mohieddine Jelali. "An approach to recursive subspace identification." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8264344.

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Chaoji, Vineet, Apirak Hoonlor, and Boleslaw K. Szymanski. "Recursive data mining for role identification." In the 5th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1456223.1456270.

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Liao. "Order recursive algorithm for ARMA identification." In IEEE International Conference on Systems Engineering. IEEE, 1989. http://dx.doi.org/10.1109/icsyse.1989.48656.

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