Literatura académica sobre el tema "Linear and Nonlinear System identification"
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Artículos de revistas sobre el tema "Linear and Nonlinear System identification"
Wang, Shuning y Masahiro Tanaka. "Nonlinear system identification with piecewise-linear functions". IFAC Proceedings Volumes 32, n.º 2 (julio de 1999): 3796–801. http://dx.doi.org/10.1016/s1474-6670(17)56648-3.
Texto completoBenyassi, Mohamed y Adil Brouri. "Identification of Nonparametric Nonlinear Systems". ITM Web of Conferences 24 (2019): 02006. http://dx.doi.org/10.1051/itmconf/20192402006.
Texto completoNakamura, Akira y Nozomu Hamada. "Identification of nonlinear dynamical system by piecewise-linear system". Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 74, n.º 9 (1991): 102–15. http://dx.doi.org/10.1002/ecjc.4430740911.
Texto completoSpanos, P. D. y R. Lu. "Nonlinear System Identification in Offshore Structural Reliability". Journal of Offshore Mechanics and Arctic Engineering 117, n.º 3 (1 de agosto de 1995): 171–77. http://dx.doi.org/10.1115/1.2827086.
Texto completoBenyassi, Mohamed, Adil Brouri y Smail Slassi. "Nonlinear systems identification with discontinuous nonlinearity". IAES International Journal of Robotics and Automation (IJRA) 9, n.º 1 (6 de marzo de 2019): 34. http://dx.doi.org/10.11591/ijra.v9i1.pp34-41.
Texto completoPotts, Duncan y Claude Sammut. "ONLINE NONLINEAR SYSTEM IDENTIFICATION USING LINEAR MODEL TREES". IFAC Proceedings Volumes 38, n.º 1 (2005): 202–7. http://dx.doi.org/10.3182/20050703-6-cz-1902.00034.
Texto completoBendat, Julius S. "Spectral Techniques for Nonlinear System Analysis and Identification". Shock and Vibration 1, n.º 1 (1993): 21–31. http://dx.doi.org/10.1155/1993/438416.
Texto completoHuang, Xiaolin, Jun Xu y Shuning Wang. "Nonlinear system identification with continuous piecewise linear neural network". Neurocomputing 77, n.º 1 (febrero de 2012): 167–77. http://dx.doi.org/10.1016/j.neucom.2011.09.001.
Texto completoPeng, Jiehua, Jiashi Tang y Zili Chen. "Parameter Identification of Weakly Nonlinear Vibration System in Frequency Domain". Shock and Vibration 11, n.º 5-6 (2004): 685–92. http://dx.doi.org/10.1155/2004/634785.
Texto completoHaroon, Muhammad, Douglas E. Adams y 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, n.º 5 (28 de marzo de 2005): 483–92. http://dx.doi.org/10.1115/1.2013293.
Texto completoTesis sobre el tema "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.
Texto completoThe 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.
Texto completoSolomou, 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.
Texto completoSouza, 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.
Texto completoAllison, Timothy Charles. "System Identification via the Proper Orthogonal Decomposition". Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29424.
Texto completoPh. D.
Raptis, Ioannis A. "Linear and Nonlinear Control of Unmanned Rotorcraft". Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/3482.
Texto completoLing, 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.
Texto completoVakakis, 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.
Texto completoCieza, 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.
Texto completoTesis
Libros sobre el tema "Linear and Nonlinear System identification"
Billings, S. A. Piecewise linear identification of nonlinear systems. Sheffield: University,Dept. of Control Engineering, 1986.
Buscar texto completoSantos, Paulo Lopes dos. Linear parameter-varying system identification: New developments and trends. Singapore: World Scientific, 2012.
Buscar texto completoCoca, 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.
Buscar texto completoTsang, K. M. Identification of multi-class linear and nonlinear systems. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.
Buscar texto completoLi, 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.
Buscar texto completoPrakriya, Shankar. Blind identification of linear and nonlinear systems with cycloststionary inputs. Ottawa: National Library of Canada, 1993.
Buscar texto completoBendat, Julius S. Nonlinear system analysis and identification from random data. New York: Wiley, 1990.
Buscar texto completoUnited States. National Aeronautics and Space Administration., ed. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. [Washington, D.C: National Aeronautics and Space Administration, 1997.
Buscar texto completoCenter, Langley Research, ed. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.
Buscar texto completoBillings, Stephen A. Nonlinear System Identification. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118535561.
Texto completoCapítulos de libros sobre el tema "Linear and Nonlinear System identification"
Nelles, Oliver. "Linear Optimization". En Nonlinear System Identification, 35–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_3.
Texto completoNelles, Oliver. "Linear Optimization". En Nonlinear System Identification, 35–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_3.
Texto completoNelles, Oliver. "Linear Dynamic System Identification". En Nonlinear System Identification, 457–546. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_14.
Texto completoNelles, Oliver. "Linear Dynamic System Identification". En Nonlinear System Identification, 715–830. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_18.
Texto completoNelles, Oliver. "Local Linear Neuro-Fuzzy Models: Fundamentals". En Nonlinear System Identification, 341–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_12.
Texto completoNelles, Oliver. "Dynamic Local Linear Neuro-Fuzzy Models". En Nonlinear System Identification, 601–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_18.
Texto completoNelles, Oliver. "Local Linear Neuro-Fuzzy Models: Fundamentals". En Nonlinear System Identification, 393–445. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_13.
Texto completoNelles, Oliver. "Dynamic Local Linear Neuro-Fuzzy Models". En Nonlinear System Identification, 919–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_22.
Texto completoNelles, Oliver. "Local Linear Neuro-Fuzzy Models: Advanced Aspects". En Nonlinear System Identification, 391–449. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_13.
Texto completoNelles, Oliver. "Linear, Polynomial, and Look-Up Table Models". En Nonlinear System Identification, 219–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_9.
Texto completoActas de conferencias sobre el tema "Linear and Nonlinear System identification"
Mandic, D. P. y J. A. Chambers. "Advanced PRNN based nonlinear prediction/system identification". En IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980446.
Texto completoHaryanto, Ade y Keum-Shik Hong. "Multi-Linear MPC for Nonlinear Oxy-Fuel Combustion Boiler System". En Artificial Intelligence and Applications / Modelling, Identification, and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-038.
Texto completoCheng, Yu y Jinglu Hu. "Nonlinear system identification based on SVR with quasi-linear kernel". En 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252694.
Texto completoGhogho, M., A. K. Nandi y A. Swami. "Identification of Volterra nonlinear systems using circular inputs". En IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980445.
Texto completoTang, Jiong, Rajamani Doraiswami y Chris P. Diduch. "Identification of a linear model for nonlinear systems". En 2009 IEEE International Conference on Control and Automation (ICCA). IEEE, 2009. http://dx.doi.org/10.1109/icca.2009.5410381.
Texto completoAfri, Chouaib, Vincent Andrieu, Laurent Bako y Pascal Dufour. "Identification of linear systems with nonlinear Luenberger Observers". En 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171853.
Texto completoVaradarajan, Nadathur P. y Satish Nagarajaiah. "Non Linear System Identification of Offshore Floating Structures". En ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57161.
Texto completoSicuranza, Giovanni L. y Alberto Carini. "Nonlinear system identification by means of mixtures of linear-in-the-parameters nonlinear filters". En 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2013. http://dx.doi.org/10.1109/ispa.2013.6703763.
Texto completoJames, Sebastian y Sean R. Anderson. "Linear System Identification of Longitudinal Vehicle Dynamics Versus Nonlinear Physical Modelling". En 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516756.
Texto completoShikimori, Takashi, Hideo Muroi y Shuichi Adachi. "A Nonlinear System Identification Method based on Local Linear PLS Method". En Intelligent Systems and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.744-026.
Texto completoInformes sobre el tema "Linear and Nonlinear System identification"
Farrar, Charles R., Keith Worden, Michael D. Todd, Gyuhae Park, Jonathon Nichols, Douglas E. Adams, Matthew T. Bement y Kevin Farinholt. Nonlinear System Identification for Damage Detection. Office of Scientific and Technical Information (OSTI), noviembre de 2007. http://dx.doi.org/10.2172/922532.
Texto completoZhang, Xi-Cheng. DURIP-94 Gigawatt The Beam System for Linear and Nonlinear Fir Spectroscopy. Fort Belvoir, VA: Defense Technical Information Center, abril de 1996. http://dx.doi.org/10.21236/ada315718.
Texto completoBergman, Lawrence A., Alexander F. Vakakis y 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, marzo de 2011. http://dx.doi.org/10.21236/ada565204.
Texto completoGoodson, T., Wang III y 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, julio de 1996. http://dx.doi.org/10.21236/ada311120.
Texto completoAltstein, Miriam y 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, octubre de 2006. http://dx.doi.org/10.32747/2006.7587235.bard.
Texto completoVisser, R., H. Kao, R. M. H. Dokht, A. B. Mahani y 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.
Texto completoEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Texto completoWu, Yingjie, Selim Gunay y Khalid Mosalam. Hybrid Simulations for the Seismic Evaluation of Resilient Highway Bridge Systems. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, noviembre de 2020. http://dx.doi.org/10.55461/ytgv8834.
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