Relevant bibliographies by topics / Linear and Nonlinear System identification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Linear and Nonlinear System identification'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Linear and Nonlinear System identification"

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Wang, Shuning, and Masahiro Tanaka. "Nonlinear system identification with piecewise-linear functions." IFAC Proceedings Volumes 32, no. 2 (1999): 3796–801. http://dx.doi.org/10.1016/s1474-6670(17)56648-3.

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Benyassi, Mohamed, and Adil Brouri. "Identification of Nonparametric Nonlinear Systems." ITM Web of Conferences 24 (2019): 02006. http://dx.doi.org/10.1051/itmconf/20192402006.

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Presently, a modelling and identification of nonlinear systems is proposed. This study is developed based on spectral approach. The proposed nonlinear system is nonparametric and can be described by Hammerstein models. These systems consist of nonlinear element followed by a linear block. This latter (the linear subsystem) is not necessarily parametric and the nonlinear function can be nonparametric smooth nonlinearity. This identification problem of Hammerstein models is studied in the presence of possibly infinite-order linear dynamics. The determination of linear and nonlinear block can be
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Nakamura, Akira, and 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.

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Benyassi, Mohamed, and Adil Brouri. "Estimation of nonlinear systems parameters." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (2020): 26–33. https://doi.org/10.11591/ijra.v9i1.pp26-33.

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In this paper, an identification method is proposed to determine the nonlinear systems parameters. The proposed nonlinear systems can be described by Wiener systems. This structure of models consists of series of linear dynamic element and a nonlinearity block. Both the linear and nonlinear parts are nonparametric. In particular, the linear subsystem of structure entirely unknown. The considered nonlinearity function is of hard type. This latter can have a dead zone or with preload. These nonlinear systems have been confirmed by several practical applications
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Li, Yingying, Tianpeng Zhang, Subhro Das, Jeff Shamma, and Na Li. "Non-asymptotic System Identification for Linear Systems with Nonlinear Policies." IFAC-PapersOnLine 56, no. 2 (2023): 1672–79. http://dx.doi.org/10.1016/j.ifacol.2023.10.1872.

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Potts, Duncan, and 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.

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Spanos, P. D., and R. Lu. "Nonlinear System Identification in Offshore Structural Reliability." Journal of Offshore Mechanics and Arctic Engineering 117, no. 3 (1995): 171–77. http://dx.doi.org/10.1115/1.2827086.

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Nonlinear forces acting on offshore structures are examined from a system identification perspective. The nonlinearities are induced by ocean waves and may become significant in many situations. They are not necessarily in the form of Morison’s equation. Various wave force models are examined. The force function is either decomposed into a set of base functions or it is expanded in terms of the wave and structural kinematics. The resulting nonlinear system is decomposed into a number of parallel no-memory nonlinear systems, each followed by a finite-memory linear system. A conditioning procedu
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Bendat, 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.

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This article reviews some recent and current research work with emphasis on new recommended spectral techniques that can analyze and identify the optimum linear and nonlinear system properties in a large class of single-input/single-output nonlinear models by using experimentally measured input/output random data. This is done by showing how to replace these nonlinear models with equivalent multiple-input/single-output linear models that are solvable by well-established practical procedures. The input random data can have probability density functions that are Gaussian or non-Gaussian with arb
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Peng, Jiehua, Jiashi Tang, and 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.

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A new method of identifying parameters of nonlinearly vibrating system in frequency domain is presented in this paper. The problems of parameter identification of the nonlinear dynamic system with nonlinear elastic force or nonlinear damping force are discussed. In the method, the mathematic model of parameter identification is frequency response function. Firstly, by means of perturbation method the frequency response function of weakly nonlinear vibration system is derived. Next, a parameter transformation is made and the frequency response function becomes a linear function of the new param
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Benyassi, Mohamed, Adil Brouri, and Smail Slassi. "Nonlinear systems identification with discontinuous nonlinearity." IAES International Journal of Robotics and Automation (IJRA) 9, no. 1 (2019): 34. http://dx.doi.org/10.11591/ijra.v9i1.pp34-41.

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<span>In this paper, nonparametric nonlinear systems identification is proposed. The considered system nonlinearity is nonparametric and is of hard type. This latter can be discontinuous and noninvertible. The entire nonlinear system is structured by Hammerstein model. Furthermore, the linear dynamic block is of any order and can be nonparametric. The problem identification method is done within two stages. In the first stage, the system nonlinearity is identified using simple input signals. In the first stage, the linear dynamic block parameters are estimated using periodic signals. The
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Dissertations / Theses on the topic "Linear and Nonlinear System identification"

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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.

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<p>The 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.</p><p>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
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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.

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Solomou, 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.

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Souza, 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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SOUZA JUNIOR, A. H. Regional models and minimal learning machines for nonlinear dynamical system identification. 2014. 116 f. Tese (Doutorado em Engenharia de Teleinformática) – Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2014.<br>Submitted by Marlene Sousa (mmarlene@ufc.br) on 2015-05-26T13:38:05Z No. of bitstreams: 1 2014_dis_ahsouzajunior.pdf: 5675945 bytes, checksum: da4cd07b3287237a51c36e519d0cae14 (MD5)<br>Approved for entry into archive by Marlene Sousa(mmarlene@ufc.br) on 2015-05-27T19:40:24Z (GMT) No. of bitstreams: 1 2014_dis_ahsouzajunior.pdf: 5675945 bytes, c
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Xi, Zhiyu Electrical Engineering &amp 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.

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In this thesis, a class of control and identification methods on a typical laboratory process - a ball and beam system - are discussed. The ball and beam is a common laboratory process which contains nonlinearity, a double integrator and time-delay. In our project, the hardware made by Wincon (Quanser SRV02 +BB01) is used. The main contribution of this work is the development of a variety of controller design methods, which together with suitable parameter identification techniques provide tools for rapid prototyping for real time control of processes within the laboratory, in preparation for
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Allison, Timothy Charles. "System Identification via the Proper Orthogonal Decomposition." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29424.

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Although the finite element method is often applied to analyze the dynamics of structures, its application to large, complex structures can be time-consuming and errors in the modeling process may negatively affect the accuracy of analyses based on the model. System identification techniques attempt to circumvent these problems by using experimental response data to characterize or identify a system. However, identification of structures that are time-varying or nonlinear is problematic because the available methods generally require prior understanding about the equations of motion for the sy
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Raptis, Ioannis A. "Linear and Nonlinear Control of Unmanned Rotorcraft." Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/3482.

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The main characteristic attribute of the rotorcraft is the use of rotary wings to produce the thrust force necessary for motion. Therefore, rotorcraft have an advantage relative to fixed wing aircraft because they do not require any relative velocity to produce aerodynamic forces. Rotorcraft have been used in a wide range of missions of civilian and military applications. Particular interest has been concentrated in applications related to search and rescue in environments that impose restrictions to human presence and interference. The main representative of the rotorcraft family is the helic
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Ling, 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.

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Three time domain system identification (SI) approaches, i.e., Modified Iterative Least Square with Unknown Input (ILS-UI), Localized Structural Identification, and Modified Iterative Least Square--Extended Kalman Filter with Unknown Input (ILS-EKF-UI), are proposed to identify defects at the element level of structures. In all these methods, structures are modeled using the finite element method (FEM) and the structural parameters (stiffness and damping) are identified using only output response measurements without using any information on input excitation. Excitations are identified as a by
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Vakakis, 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.

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Cieza, 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.

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At the present, system identification through modulation functions has a wide range of methods. Many of them have reached maturity levels that surpass customary Kalmanfilter approaches for discrete-time identification. In this thesis, the modulation function technique is analyzed in view of its real-time capability, as well as the possible unification of the modulation function methods based on the frequency spectrum, and ability to deal with nonlinearities. Besides, to increase the rate of convergence, the optimal parameter estimation with constraints of Byrski et al. [BFN03] is applied
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Books on the topic "Linear and Nonlinear System identification"

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Santos, Paulo Lopes dos. Linear parameter-varying system identification: New developments and trends. World Scientific, 2012.

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Billings, S. A. Piecewise linear identification of nonlinear systems. University,Dept. of Control Engineering, 1986.

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Coca, D. Continuous-time system identification for linear and nonlinear systems using wavelet decomposition. University of Sheffield, Department of Automatic Control and Systems Engineering, 1996.

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Tsang, K. M. Identification of multi-class linear and nonlinear systems. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1991.

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Li, L. M. Continuous time linear and nonlinear system identification in the frequency domain. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

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Prakriya, Shankar. Blind identification of linear and nonlinear systems with cycloststionary inputs. National Library of Canada, 1993.

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Bendat, Julius S. Nonlinear system analysis and identification from random data. Wiley, 1990.

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Center, Langley Research, ed. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. National Aeronautics and Space Administration, Langley Research Center, 1997.

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United States. National Aeronautics and Space Administration., ed. Identification of linear and nonlinear aerodynamic impulse responses using digital filter techniques. National Aeronautics and Space Administration, 1997.

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Billings, Stephen A. Nonlinear System Identification. John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118535561.

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Book chapters on the topic "Linear and Nonlinear System identification"

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Nelles, Oliver. "Linear Optimization." In Nonlinear System Identification. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_3.

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Nelles, Oliver. "Linear Optimization." In Nonlinear System Identification. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_3.

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Nelles, Oliver. "Linear Dynamic System Identification." In Nonlinear System Identification. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_14.

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Nelles, Oliver. "Linear Dynamic System Identification." In Nonlinear System Identification. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_18.

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Nelles, Oliver. "Local Linear Neuro-Fuzzy Models: Fundamentals." In Nonlinear System Identification. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_12.

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Nelles, Oliver. "Dynamic Local Linear Neuro-Fuzzy Models." In Nonlinear System Identification. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_18.

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Nelles, Oliver. "Local Linear Neuro-Fuzzy Models: Fundamentals." In Nonlinear System Identification. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_13.

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Nelles, Oliver. "Dynamic Local Linear Neuro-Fuzzy Models." In Nonlinear System Identification. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_22.

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Nelles, Oliver. "Local Linear Neuro-Fuzzy Models: Advanced Aspects." In Nonlinear System Identification. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_13.

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Nelles, Oliver. "Linear, Polynomial, and Look-Up Table Models." In Nonlinear System Identification. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_9.

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Conference papers on the topic "Linear and Nonlinear System identification"

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Zhang, Wenbo, Xiaoyue Guo, Bo Zhang, Yunpeng Zhu, Liangliang Cheng, and Zhike Peng. "Parameter Identification of Nonlinear Systems Using Generalized Associated Linear Equations (GALEs)." In 2024 6th International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2024. http://dx.doi.org/10.1109/iai63275.2024.10730096.

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Greiser, Steffen, and Wolfgang Grünhagen. "Improving System Identification Results: Combining a Physics-Based Stitched Model with Transfer Function Models Obtained Through Inverse Simulation." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11497.

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It is state-of-the-art to model helicopter dynamics by low-order equivalent linear systems at certain speed-dependent operating points for the flight envelope of interest. These models are obtained from flight test data by system identification. Based on the linear models, it is desired to simulate maneuvering flight such as acceleration and deceleration between hover and high forward-speed. Additionally, the identified operating point model should consist of a high modeling accuracy. Thus, the two modeling and simulation goals are: generation of a full-envelope simulation by embedding all lin
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Mandic, D. P., and J. A. Chambers. "Advanced PRNN based nonlinear prediction/system identification." In IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980446.

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Haryanto, Ade, and Keum-Shik Hong. "Multi-Linear MPC for Nonlinear Oxy-Fuel Combustion Boiler System." In Artificial Intelligence and Applications / Modelling, Identification, and Control. ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-038.

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Cheng, Yu, and Jinglu Hu. "Nonlinear system identification based on SVR with quasi-linear kernel." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252694.

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Varadarajan, Nadathur P., and Satish Nagarajaiah. "Non Linear System Identification of Offshore Floating Structures." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57161.

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Floating structures such as spar platforms are typically designed to be compliant or move with environment loadings, rather than resisting them. Hence they are designed so that there is no dynamic amplification in wave frequency response. However, higher order nonlinear effects are produced in low frequency wind excited regions, especially in Spar platforms. It is difficult to separate the nonlinear behavior of the model response from the loading using conventional methods. In this paper, Empirical Mode Decomposition and Hilbert Transform (EMD/HT) is used to identify the nonlinear response of
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Tang, Jiong, Rajamani Doraiswami, and Chris P. Diduch. "Identification of a linear model for nonlinear systems." In 2009 IEEE International Conference on Control and Automation (ICCA). IEEE, 2009. http://dx.doi.org/10.1109/icca.2009.5410381.

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Afri, Chouaib, Vincent Andrieu, Laurent Bako, and Pascal Dufour. "Identification of linear systems with nonlinear Luenberger Observers." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171853.

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Ghogho, M., A. K. Nandi, and A. Swami. "Identification of Volterra nonlinear systems using circular inputs." In IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980445.

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Sicuranza, Giovanni L., and Alberto Carini. "Nonlinear system identification by means of mixtures of linear-in-the-parameters nonlinear filters." In 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2013. http://dx.doi.org/10.1109/ispa.2013.6703763.

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Reports on the topic "Linear and Nonlinear System identification"

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Farrar, Charles R., Keith Worden, Michael D. Todd, et al. Nonlinear System Identification for Damage Detection. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/922532.

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Zhang, Xi-Cheng. DURIP-94 Gigawatt The Beam System for Linear and Nonlinear Fir Spectroscopy. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada315718.

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Bergman, Lawrence A., Alexander F. Vakakis, and D. M. McFarland. Acquisition of a Scanning Laser Vibrometer System for Experimental Studies on Nonparametric Nonlinear System Identification and Aeroelastic Instability Suppression. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada565204.

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Goodson, T., Wang III, and C. H. Dispersion and Dipolar Orientational Effects on the Linear Electro-Absorption and Electro-Optic Responses in a Model Guest/Host Nonlinear Optical System. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada311120.

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Agarwala, Matthew, Matt Burke, Jennifer Doherty-Bigara, Patrycja Klusak, and Kamiar Mohaddes. Climate Change and Sovereign Risk: A Regional Analysis for the Caribbean. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0012885.

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Climate change is an existential threat to the world economy, with complex, evolving and nonlinear dynamics that remain a source of great uncertainty. There is a bourgeoning literature on the economic impact of climate change, but research on how climate change affects sovereign risks is limited. This paper provides forward-looking regional analysis of the effects of climate change on sovereign creditworthiness, probability of default and the cost of borrowing for the Caribbean economies. Our results indicate that there is substantial variation in the sensitivity of ratings to climate change a
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Lozada, Gabriel A. The Perils of Antitrust Econometrics: Unrealistic Engel Curves, Inadequate Data, and Aggregation Bias. Institute for New Economic Thinking Working Paper Series, 2023. http://dx.doi.org/10.36687/inetwp203.

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Some economists argue antitrust policy should be based on empirical methods used by the Industrial Organization subdiscipline of economics, but non-economists must understand that those methods contain certain highly restrictive assumptions. Those assumptions involve econometric “identification,” and treating aggregate demand as if it were generated by a representative consumer (Muellbauer’s “generalized linear” preferences). We derive new results illustrating how restrictive the representative consumer assumption is; we explain aggregation bias in Almost Ideal Demand System models; and we sho
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l
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Altstein, Miriam, and 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, 2006. http://dx.doi.org/10.32747/2006.7587235.bard.

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The general objective of this BARD project focused on rationally designed insect neuropeptide (NP) agonists and antagonists, their application for the characterization of the mechanisms of action of the pyrokinin/PBAN (PK-PBAN) family and the development of biostable, bioavailable versions that can provide the basis for development of novel, environmentally-friendly pest insect control agents. The specific objectives of the study, as originally proposed, were to: (i) Test stimulatory potencies of rationally designed backbone cyclic (BBC) peptides on pheromonotropic, melanotropic, myotropic and
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Visser, R., H. Kao, R. M. H. Dokht, A. B. Mahani, and 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.

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To increase our understanding of induced seismicity, we develop and implement methods to enhance seismic monitoring capabilities in northeastern British Columbia (NE BC). We deploy two different machine learning models to identify earthquake phases using waveform data from regional seismic stations and utilize an earthquake database management system to streamline the construction and maintenance of an up-to-date earthquake catalogue. The completion of this study allows for a comprehensive catalogue in NE BC from 2014 to 2020 by building upon our previous 2014-2016 and 2017-2018 catalogues. Th
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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