Results 211 to 220 of about 1,346,541 (251)

Identification of Nonlinear Systems

1989 American Control Conference, 1989
It is extremely difficult to identify general nonlinear systems because of the number of unknowns involved. Moreover, since most design techniques assume a linear model, many of the nonlinearities that can be determined are essentially ignored. Why not develop nonlinear system identification techniques to reveal restricted classes of nonlinear systems ...
L.R. Hunt, R.D. DeGroat, D.A. Linebarger
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Nonlinear system identification: An overview

1993
System identification consists in the characterization of a system from an analysis of observed input and output signals. In essence, the ultimate aim of system identification is prediction such that given a description of the system transfer parameters and the input, the output can be completely specified for any time.
Zoubir, AM, Boashash, B
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Nonlinear System Identification: An Overview of Common Approaches

2014
Nonlinear mathematical models are essential tools in various engineering and scientific domains, where more and more data are recorded by electronic devices. How to build nonlinear mathematical models essentially based on experimental data is the topic of this entry.
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On the Identification of Nonlinear Systems

2000
In many engineering disciplines the identification of dynamical systems from measured signals is an important task in the modelling process. If information on the structure of the system is available, the task is reduced to the identification of parameters. Often, however, either such information is not available or the system structure is known but is
K. Popp, J.-U. Bruns
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Characteristic nonlinear system identification of local attachments with clearance nonlinearities

Nonlinear dynamics, 2020
Aryan Singh, K. Moore
semanticscholar   +1 more source

Identification of Nonlinear Systems

1982
At first glance few problems arise in the area of structural dynamics which cannot be solved by means of today’s experimental and analytical tools. Thus, the elastodynamic characteristics can be determined by using common experimental or analytical methods if structural linearity can be assumed to be a proper approximation.
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Nonlinear System Identification

2010
This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended ...
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Nonlinear system identification using fractional Hammerstein–Wiener models

Nonlinear dynamics, 2019
Karima Hammar, T. Djamah, M. Bettayeb
semanticscholar   +1 more source

Identification of nonlinear systems having discontinuous nonlinearity

International Journal of Modelling, Identification and Control, 2019
Adil Brouri, Laila Kadi, Mohamed Benyassi   +2 more
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Identification of nonlinear system composed of parallel coupling of Wiener and Hammerstein models

Asian Journal of Control, 2022
Adil Brouri, Laila Kadi
exaly