Littérature scientifique sur le sujet « Linear and Nonlinear System identification »
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Articles de revues sur le sujet "Linear and Nonlinear System identification"
Wang, Shuning, et Masahiro Tanaka. « Nonlinear system identification with piecewise-linear functions ». IFAC Proceedings Volumes 32, no 2 (juillet 1999) : 3796–801. http://dx.doi.org/10.1016/s1474-6670(17)56648-3.
Texte intégralBenyassi, Mohamed, et Adil Brouri. « Identification of Nonparametric Nonlinear Systems ». ITM Web of Conferences 24 (2019) : 02006. http://dx.doi.org/10.1051/itmconf/20192402006.
Texte intégralNakamura, Akira, et Nozomu Hamada. « Identification of nonlinear dynamical system by piecewise-linear system ». Electronics and Communications in Japan (Part III : Fundamental Electronic Science) 74, no 9 (1991) : 102–15. http://dx.doi.org/10.1002/ecjc.4430740911.
Texte intégralSpanos, P. D., et R. Lu. « Nonlinear System Identification in Offshore Structural Reliability ». Journal of Offshore Mechanics and Arctic Engineering 117, no 3 (1 août 1995) : 171–77. http://dx.doi.org/10.1115/1.2827086.
Texte intégralBenyassi, Mohamed, Adil Brouri et Smail Slassi. « Nonlinear systems identification with discontinuous nonlinearity ». IAES International Journal of Robotics and Automation (IJRA) 9, no 1 (6 mars 2019) : 34. http://dx.doi.org/10.11591/ijra.v9i1.pp34-41.
Texte intégralPotts, Duncan, et Claude Sammut. « ONLINE NONLINEAR SYSTEM IDENTIFICATION USING LINEAR MODEL TREES ». IFAC Proceedings Volumes 38, no 1 (2005) : 202–7. http://dx.doi.org/10.3182/20050703-6-cz-1902.00034.
Texte intégralBendat, Julius S. « Spectral Techniques for Nonlinear System Analysis and Identification ». Shock and Vibration 1, no 1 (1993) : 21–31. http://dx.doi.org/10.1155/1993/438416.
Texte intégralHuang, Xiaolin, Jun Xu et Shuning Wang. « Nonlinear system identification with continuous piecewise linear neural network ». Neurocomputing 77, no 1 (février 2012) : 167–77. http://dx.doi.org/10.1016/j.neucom.2011.09.001.
Texte intégralPeng, Jiehua, Jiashi Tang et Zili Chen. « Parameter Identification of Weakly Nonlinear Vibration System in Frequency Domain ». Shock and Vibration 11, no 5-6 (2004) : 685–92. http://dx.doi.org/10.1155/2004/634785.
Texte intégralHaroon, Muhammad, Douglas E. Adams et Yiu Wah Luk. « A Technique for Estimating Linear Parameters Using Nonlinear Restoring Force Extraction in the Absence of an Input Measurement ». Journal of Vibration and Acoustics 127, no 5 (28 mars 2005) : 483–92. http://dx.doi.org/10.1115/1.2013293.
Texte intégralThèses sur le sujet "Linear and Nonlinear System identification"
Gransten, Johan. « Linear and Nonlinear Identification of Solid Fuel Furnace ». Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5182.
Texte intégralThe aim of this thesis is to develop the knowledge about nonlinear and/or adaptive solid fuel boiler control at Vattenfall Utveckling AB. The aim is also to make a study of implemented and published control strategies.
A solid fuel boiler is a large-scale heat (and power) generating plant. The Idbäcken boiler studied in this work, is a one hundred MW furnace mainly fired with wood chips. The control system consists of several linear PID controllers working together, and the furnace is a nonlinear system. That, and the fact that the fuel-flow is not monitored, are the main reasons for the control problems. The system fluctuates periodically and the CO outlets sometimes rise high above the permitted level.
There is little work done in the area of advanced boiler control, but some interesting approaches are described in scientific articles. MPC (Model Predictive Control), nonlinear system identification using ANN (Artificial Neural Network), fuzzy logic, Hµ loop shaping and MIMO (Multiple Input Multiple Output) PID tuning methods have been tested with good results.
Both linear and nonlinear system identification is performed in the thesis. The linear models are able to explain about forty percent of the system behavior and the nonlinear models explain about sixty to eighty percent. The main result is that nonlinear models improve the performance and that there are considerable disturbances complicating the identification. Another identification issue was the feedback during the data collection.
Enqvist, Martin. « Linear Models of Nonlinear Systems ». Doctoral thesis, Linköping : Linköpings universitet, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5330.
Texte intégralSolomou, Michael. « System identification in the presence of nonlinear distortions using multisine signals ». Thesis, University of South Wales, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289160.
Texte intégralSouza, Júnior Amauri Holanda de. « Regional models and minimal learning machines for nonlinear dynamical system identification ». reponame:Repositório Institucional da UFC, 2014. http://www.repositorio.ufc.br/handle/riufc/12481.
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This thesis addresses the problem of identifying nonlinear dynamic systems from a machine learning perspective. In this context, very little is assumed to be known about the system under investigation, and the only source of information comes from input/output measurements on the system. It corresponds to the black-box modeling approach. Numerous strategies and models have been proposed over the last decades in the machine learning field and applied to modeling tasks in a straightforward way. Despite of this variety, the methods can be roughly categorized into global and local modeling approaches. Global modeling consists in fitting a single regression model to the available data, using the whole set of input and output observations. On the other side of the spectrum stands the local modeling approach, in which the input space is segmented into several small partitions and a specialized regression model is fit to each partition. The first contribution of the thesis is a novel supervised global learning model, the Minimal Learning Machine (MLM). Learning in MLM consists in building a linear mapping between input and output distance matrices and then estimating the nonlinear response from the geometrical configuration of the output points. Given its general formulation, the Minimal Learning Machine is inherently capable of operating on nonlinear regression problems as well as on multidimensional response spaces. Naturally, its characteristics make the MLM able to tackle the system modeling problem. The second significant contribution of the thesis represents a different modeling paradigm, called Regional Modeling (RM), and it is motivated by the parsimonious principle. Regional models stand between the global and local modeling approaches. The proposal consists of a two-level clustering approach in which we first partition the input space using the Self-Organizing Map (SOM), and then perform clustering over the prototypes of the trained SOM. After that, regression models are built over the clusters of SOM prototypes, or regions in the input space. Even though the proposals of the thesis can be thought as quite general regression or supervised learning models, the performance assessment is carried out in the context of system identification. Comprehensive performance evaluation of the proposed models on synthetic and real-world datasets is carried out and the results compared to those achieved by standard global and local models. The experiments illustrate that the proposed methods achieve accuracies that are comparable to, and even better than, more traditional machine learning methods thus offering a valid alternative to such approaches
Xi, Zhiyu Electrical Engineering & Telecommunications Faculty of Engineering UNSW. « Identification and control of nonlinear laboratory processes ». Awarded by:University of New South Wales. Electrical Engineering & ; Telecommunications, 2007. http://handle.unsw.edu.au/1959.4/40461.
Texte intégralAllison, Timothy Charles. « System Identification via the Proper Orthogonal Decomposition ». Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29424.
Texte intégralPh. D.
Raptis, Ioannis A. « Linear and Nonlinear Control of Unmanned Rotorcraft ». Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/3482.
Texte intégralLing, Xiaolin. « Linear and nonlinear time domain system identification at element level for structural systems with unknown excitation ». Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/284163.
Texte intégralVakakis, Alexander F. Caughey Thomas Kirk. « Analysis and identification of linear and nonlinear normal modes in vibrating systems / ». Diss., Pasadena, Calif. : California Institute of Technology, 1991. http://resolver.caltech.edu/CaltechETD:etd-08232004-105610.
Texte intégralCieza, Aguirre Oscar Benjamín. « Rapid continuous-time identification of linear and nonlinear systems using modulation function approaches ». Master's thesis, Pontificia Universidad Católica del Perú, 2015. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8123.
Texte intégralTesis
Livres sur le sujet "Linear and Nonlinear System identification"
Billings, S. A. Piecewise linear identification of nonlinear systems. Sheffield : University,Dept. of Control Engineering, 1986.
Trouver le texte intégralSantos, Paulo Lopes dos. Linear parameter-varying system identification : New developments and trends. Singapore : World Scientific, 2012.
Trouver le texte intégralCoca, D. Continuous-time system identification for linear and nonlinear systems using wavelet decomposition. Sheffield : University of Sheffield, Department of Automatic Control and Systems Engineering, 1996.
Trouver le texte intégralTsang, K. M. Identification of multi-class linear and nonlinear systems. Sheffield : University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.
Trouver le texte intégralLi, L. M. Continuous time linear and nonlinear system identification in the frequency domain. Sheffield : University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.
Trouver le texte intégralPrakriya, Shankar. Blind identification of linear and nonlinear systems with cycloststionary inputs. Ottawa : National Library of Canada, 1993.
Trouver le texte intégralBendat, Julius S. Nonlinear system analysis and identification from random data. New York : Wiley, 1990.
Trouver le texte intégralUnited States. National Aeronautics and Space Administration., dir. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. [Washington, D.C : National Aeronautics and Space Administration, 1997.
Trouver le texte intégralCenter, Langley Research, dir. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. Hampton, Va : National Aeronautics and Space Administration, Langley Research Center, 1997.
Trouver le texte intégralBillings, Stephen A. Nonlinear System Identification. Chichester, UK : John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118535561.
Texte intégralChapitres de livres sur le sujet "Linear and Nonlinear System identification"
Nelles, Oliver. « Linear Optimization ». Dans Nonlinear System Identification, 35–77. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_3.
Texte intégralNelles, Oliver. « Linear Optimization ». Dans Nonlinear System Identification, 35–92. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_3.
Texte intégralNelles, Oliver. « Linear Dynamic System Identification ». Dans Nonlinear System Identification, 457–546. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_14.
Texte intégralNelles, Oliver. « Linear Dynamic System Identification ». Dans Nonlinear System Identification, 715–830. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_18.
Texte intégralNelles, Oliver. « Local Linear Neuro-Fuzzy Models : Fundamentals ». Dans Nonlinear System Identification, 341–89. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_12.
Texte intégralNelles, Oliver. « Dynamic Local Linear Neuro-Fuzzy Models ». Dans Nonlinear System Identification, 601–44. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_18.
Texte intégralNelles, Oliver. « Local Linear Neuro-Fuzzy Models : Fundamentals ». Dans Nonlinear System Identification, 393–445. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_13.
Texte intégralNelles, Oliver. « Dynamic Local Linear Neuro-Fuzzy Models ». Dans Nonlinear System Identification, 919–70. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_22.
Texte intégralNelles, Oliver. « Local Linear Neuro-Fuzzy Models : Advanced Aspects ». Dans Nonlinear System Identification, 391–449. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_13.
Texte intégralNelles, Oliver. « Linear, Polynomial, and Look-Up Table Models ». Dans Nonlinear System Identification, 219–38. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_9.
Texte intégralActes de conférences sur le sujet "Linear and Nonlinear System identification"
Mandic, D. P., et J. A. Chambers. « Advanced PRNN based nonlinear prediction/system identification ». Dans IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980446.
Texte intégralHaryanto, Ade, et Keum-Shik Hong. « Multi-Linear MPC for Nonlinear Oxy-Fuel Combustion Boiler System ». Dans Artificial Intelligence and Applications / Modelling, Identification, and Control. Calgary,AB,Canada : ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-038.
Texte intégralCheng, Yu, et Jinglu Hu. « Nonlinear system identification based on SVR with quasi-linear kernel ». Dans 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252694.
Texte intégralGhogho, M., A. K. Nandi et A. Swami. « Identification of Volterra nonlinear systems using circular inputs ». Dans IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980445.
Texte intégralTang, Jiong, Rajamani Doraiswami et Chris P. Diduch. « Identification of a linear model for nonlinear systems ». Dans 2009 IEEE International Conference on Control and Automation (ICCA). IEEE, 2009. http://dx.doi.org/10.1109/icca.2009.5410381.
Texte intégralAfri, Chouaib, Vincent Andrieu, Laurent Bako et Pascal Dufour. « Identification of linear systems with nonlinear Luenberger Observers ». Dans 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171853.
Texte intégralVaradarajan, Nadathur P., et Satish Nagarajaiah. « Non Linear System Identification of Offshore Floating Structures ». Dans ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57161.
Texte intégralSicuranza, Giovanni L., et Alberto Carini. « Nonlinear system identification by means of mixtures of linear-in-the-parameters nonlinear filters ». Dans 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2013. http://dx.doi.org/10.1109/ispa.2013.6703763.
Texte intégralJames, Sebastian, et Sean R. Anderson. « Linear System Identification of Longitudinal Vehicle Dynamics Versus Nonlinear Physical Modelling ». Dans 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516756.
Texte intégralShikimori, Takashi, Hideo Muroi et Shuichi Adachi. « A Nonlinear System Identification Method based on Local Linear PLS Method ». Dans Intelligent Systems and Control. Calgary,AB,Canada : ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.744-026.
Texte intégralRapports d'organisations sur le sujet "Linear and Nonlinear System identification"
Farrar, Charles R., Keith Worden, Michael D. Todd, Gyuhae Park, Jonathon Nichols, Douglas E. Adams, Matthew T. Bement et Kevin Farinholt. Nonlinear System Identification for Damage Detection. Office of Scientific and Technical Information (OSTI), novembre 2007. http://dx.doi.org/10.2172/922532.
Texte intégralZhang, Xi-Cheng. DURIP-94 Gigawatt The Beam System for Linear and Nonlinear Fir Spectroscopy. Fort Belvoir, VA : Defense Technical Information Center, avril 1996. http://dx.doi.org/10.21236/ada315718.
Texte intégralBergman, Lawrence A., Alexander F. Vakakis et D. M. McFarland. Acquisition of a Scanning Laser Vibrometer System for Experimental Studies on Nonparametric Nonlinear System Identification and Aeroelastic Instability Suppression. Fort Belvoir, VA : Defense Technical Information Center, mars 2011. http://dx.doi.org/10.21236/ada565204.
Texte intégralGoodson, T., Wang III et C. H. Dispersion and Dipolar Orientational Effects on the Linear Electro-Absorption and Electro-Optic Responses in a Model Guest/Host Nonlinear Optical System. Fort Belvoir, VA : Defense Technical Information Center, juillet 1996. http://dx.doi.org/10.21236/ada311120.
Texte intégralAltstein, Miriam, et Ronald Nachman. Rationally designed insect neuropeptide agonists and antagonists : application for the characterization of the pyrokinin/Pban mechanisms of action in insects. United States Department of Agriculture, octobre 2006. http://dx.doi.org/10.32747/2006.7587235.bard.
Texte intégralVisser, R., H. Kao, R. M. H. Dokht, A. B. Mahani et S. Venables. A comprehensive earthquake catalogue for northeastern British Columbia : the northern Montney trend from 2017 to 2020 and the Kiskatinaw Seismic Monitoring and Mitigation Area from 2019 to 2020. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329078.
Texte intégralEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak et Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, juillet 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Texte intégralWu, Yingjie, Selim Gunay et Khalid Mosalam. Hybrid Simulations for the Seismic Evaluation of Resilient Highway Bridge Systems. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, novembre 2020. http://dx.doi.org/10.55461/ytgv8834.
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