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A new procedure of non-linear system identification is presented.
System identification is an essential part of control design.
System identification is the task of creating models of systems, using data about their behaviors.
The aim of "System Identification" is to determine modal and system properties of structural systems.
Non-linear system identification is used to generate models of modes in physical structures.
Deconvolution filtering where the system and noise dynamics are obtained by parametric system identification is considered.
A key issue in system identification is how to cope with high system complexity.
In this paper, the effect of nonlinear distortion on system identification is studied.
Here, the optimal input design in system identification is investigated as a multi-objective optimization problem.
One of the major objectives of system identification is accurate estimation of the system parameters.
Adaptive system identification is shown to be the central theme of adaptive signal processing.
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