Results 271 to 280 of about 519,710 (307)

Identification and modeling of nonlinear systems

Nuclear Engineering and Design, 1982
Abstract A nonparametric identification technique is presented for use with discrete multidegree of freedom nonlinear dynamic systems of the type encountered in nuclear reactor technology. The method requires information regarding the system response and estimates of its pertinent “mode shapes” to determine, by means of regression techniques ...
S.F. Masri, H. Sassi, T.K. Caughey
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Subsystem identification for nonlinear model updating

2006 American Control Conference, 2006
We consider model updating by adding correction terms to the model equations in the state space form. Two classes of errors, namely model errors in the dynamics equation and model errors in the output equation, are considered. The model errors are assumed to arise from an unknown nonlinear subsystem.
H.J. Palanthandalam-Madapusi, S. Gillijns, B. De Moor, D.S. Bernstein   +3 more
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Evolutionary fuzzy models for nonlinear identification

Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012), 2012
This paper proposes a new method for identification problems for industrial applications based on a Takagi-Sugeno (T-S) fuzzy model. The learning of the T-S model is performed from input/output data to approximate unknown nonlinear processes by a coevolationary genetic algorithm (GA).
Jérôme Mendes, Samuel Pinto, Rui Araújo, Francisco Souza   +3 more
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Nonlinear model identification for synchronous machine

2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009
Application of Wiener-Neural model for identification of a synchronous generator is investigated in this paper. The proposed method is applied on a simulated synchronous generator with saturation effect. In this study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the ...
Mohamad Hosein Amralahi, S. Mohamad Azimi, Yazdan Najafi Sarem, Javad Poshtan   +3 more
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Parameters nonlinear identification for vehicle's model

Proceedings of the 1997 IEEE International Conference on Control Applications, 2002
This paper examines part of a driving advice system developed by the ProArt France team (part of the European Eureka Prometheus programme). The least-squares identification of parameters for a nonlinear vehicle model is presented. We also discuss the uniqueness of the system representation (identifiability) and sensitivity functions for determining the
A. Alloum, A. Charara, H. Machkour
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Model identification of nonlinear sputter processes

2017 17th International Conference on Control, Automation and Systems (ICCAS), 2017
A nonlinear control-oriented model for sputter processes based on artificial neural networks and ordinary differential equations is developed. The process is analyzed by use of first-principle models to approximate the process structure. Hence, sputter processes can be described by a static nonlinearity, which results from the plasma processes, and ...
Christian Woelfel, Sven Kockmann, Peter Awakowicz, Jan Lunze   +3 more
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Experimental Nonlinear Model Identification of a Highly Nonlinear Resonator

Journal of Vibration and Acoustics, 2018
In this work, two model identification methods are used to estimate the nonlinear large deformation behavior of a nonlinear resonator in the time and frequency domains. A doubly clamped beam with a slender geometry carrying a central intraspan mass when subject to a transverse excitation is used as the highly nonlinear resonator.
Yildirim, Tanju, Zhang, Jiawei, Sun, Shuaishuai, Alici, Gursel, Zhang, Shiwu, Li, Weihua   +5 more
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Testing for weak identification in possibly nonlinear models

Journal of Econometrics, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Inoue, Atsushi, Rossi, Barbara
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System Identification Using Nonlinear Structural Models

1988
Analytical modeling of structures subjected to ground motions is an important aspect of fully dynamic earthquake-resistant design. In general, linear models are only sufficient to represent structural responses resulting from earthquake motions of small amplitudes. However, the response of structures during strong ground motions is highly nonlinear and
P. Jayakumar, J. L. Beck
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Covariance methods in nonlinear model identification

2018
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] In many practical engineering and machine learning applications, it is necessary to identify an unknown transformation that maps a known or measured set of input states to a known or measured set of output states.
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