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Identification of nonlinear systems described by Hammerstein models
42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), 2004This paper deals with a method for identification of nonlinear systems suitable to be described by Hammerstein models consisting of a static nonlinearity followed by an ARX linear model. The estimation of the static nonlinearity is carried out supplying the system with a sequence of step signals of various amplitude and determining the corresponding ...
ALONGE, Francesco +3 more
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Nonlinear model identification for Artemia population motion
Nonlinear Dynamics, 2012In this paper, two different nonlinear models for Artemia swarming are derived. In order to generate the data suitable for identification, a robot driving the Artemia population has been built. The obtained data have been then used to identify the parameters of a model based on Newton’s equations and a black-box NARX model implemented by neural ...
M. T. Rashid +5 more
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Nonlinear parameter identification of models for masonry
International Journal of Solids and Structures, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Identification of a nonlinear compressor model
Mathematics and Computers in Simulation, 1998In this article, a technique for the identification of a nonlinear compressor performance model exhibiting bifurcation phenomena is presented. The approach consists of employing the modal form of the compressor flow equations along with derived linear and nonlinear stability results.
C.J Rivera, J.V.R Prasad
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Modeling and identification of freeplay nonlinearity
Journal of Sound and Vibration, 2012Abstract A nonlinear analysis is performed for the purpose of identification of the pitch freeplay nonlinearity and its effect on the type of bifurcation of a two degree-of-freedom aeroelastic system. The databases for the identification are generated from experimental investigations of a pitch-plunge rigid airfoil supported by a nonlinear torsional ...
A. Abdelkefi +3 more
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Nonlinear Subspace Model Identification
IFAC Proceedings Volumes, 2004Abstract Canonical variates state space (CVSS) modeling is a popular subspace linear model identification technique. A nonlinear extension of CVSS modeling approach was proposed (DeCicco and Cinar, 2000) . The modeling procedure consists of two steps: development of a multivariable nonlinear model for a set of latent variables and the linking of the ...
Ali Cinar, Jeffrey DeCicco
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Identification and modeling of nonlinear systems
Nuclear Engineering and Design, 1982Abstract 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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Structure identification for nonlinear models
IFAC Proceedings Volumes, 2001A block-oriented nonlinear system consists of a series of blocks. These blocks represent both memoryless nonlinearity and dynamic linear functional that comprise the overall input-output dynamics of the system. Under this category, the non linear systems are represented as Wiener, Hammerstein, Wiener-Hammerstein, and HammersteinWiener models, etc ...
Hsiao-Ping Huang +2 more
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Neural modeling and identification of nonlinear systems
Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 2002This paper provides a brief overview of a rigorous framework, developed by the author, for the modeling and identification of nonlinear dynamical systems by artificial neural networks. The system model is obtained as a best approximation of the operator(s) representing the system in a "neural space", under interpolating or smoothing constraints imposed
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Identification of a nonlinear driver model via NARMAX modeling
Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000Most existing driver steering models consist of a linear function and a time delay element. Driving simulator data, however, indicates the possible existence of "complacency" nonlinearity. Three possible nonlinear structures are investigated to model this effect. The effectiveness of each structure in reducing the model uncertainty is evaluated.
Liang-Kuang Chen, A. Galip Ulsoy
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