Results 291 to 300 of about 1,828,697 (335)

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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Structured nonlinear system identification

2015
To obtain an identified model from data, the system identification practitioner has to make an important choice: to specify the set of candidate models, or model structure. This choice can play an outsized role on the success or failure of the identification process.
Vincent, Tyrone, Poolla, Kameshwar, NOVARA, Carlo   +2 more
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Identification methods for nonlinear stochastic systems

Physical Review E, 2002
Model identifications based on orbit tracking methods are here extended to stochastic differential equations. In the present approach, deterministic and statistical features are introduced via the time evolution of ensemble averages and variances. The aforementioned quantities are shown to follow deterministic equations, which are explicitly written ...
Jose-Maria, Fullana, Maurice, Rossi
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Nonlinear systems identification: autocorrelation vs. autoskewness

Journal of Applied Physiology, 1997
Sammon, Michel, and Frederick Curley. Nonlinear systems identification: autocorrelation vs. autoskewness. J. Appl. Physiol. 83(3): 975–993, 1997.—Autocorrelation function ( C 1) or autoregressive model parameters are often estimated for temporal analysis of physiological measurements.
Sammon, Michel, Curley, Frederick J.
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Nonlinear Dynamic System Identification

2001
This chapter gives an overview of the concepts for identification of nonlinear dynamic systems. Basic approaches and properties are discussed that are independent of the specific choice of the model architecture. Thus, this chapter is the foundation for both classical polynomial based and modern neural network and fuzzy based nonlinear dynamic models.
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Nonlinear structural system identification

2014
System identification techniques has been extensively developed during the past twenty years mostly because of a large number of applications in diverse fields like electric power systems, hydrology, aeronautics, astronautics, mechanical engineering and structural engineering. However, most of the techniques developed so far are based on the assumption
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Parameter Identification of Nonlinear Descriptor Systems

1993
This paper is concerned with the off-line parameter identification for nonlinear mechanical systems, described by differential-algebraic equations. The maximum likelihood method leads to a nonlinear programming problem. Some aspects of the problem formulation with respect to the descriptor form are treated.
Grupp, Friedemann, Kortüm, Willi
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Identification of Hammerstein Nonlinear Stochastic Systems

Automation and Remote Control, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Parametric identification of nonlinear dynamic systems

Computers & Structures, 1985
A parametric identification technique that exploits nonlinear resonances and comparisons of the behavior of the system to be identified with those of known systems is proposed. The mathematical model is chosen in such a way that its predicted response qualitatively resembles observed responses of the physical system to chosen excitations.
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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