Real and Complex Analysis

System Identification I

Fast Fourier Transform

2005년 대한 생화학·분자생물학회 하계학술대회를 다녀와서

This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods havean output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.

Stochastic System Identification for Operational Modal Analysis: A Review

This paper gives a survey of frequency domain identification methods for rational transfer functions in the Laplace (s) or z-domain. The interrelations between the different approaches are highlighted through a study of the (equivalent) cost functions. The properties of the various estimators are discussed and illustrated by several examples. >

Parametric identification of transfer functions in the frequency domain-a survey

Identification of linear systems with nonlinear distortions

This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.

Identification of Linear Systems with Nonlinear Distortions

System identification is a fundamentally experimental field of science in that it deals with modeling of system dynamics using measured data. Despite this fact many algorithms and theoretical results are only tested with simulations at the time of publication. One reason for this may be a lack of easily available live data. This paper therefore presents three sets of data, suitable for development, testing and benchmarking of system identification algorithms for nonlinear systems. The data sets are collected from laboratory processes that can be described by block - oriented dynamic models, and by more general nonlinear difference and differential equation models. All data sets are available for free download.

Johan Schoukens

Papers

Parametric identification of transfer functions in the frequency domain-a survey

Identification of linear systems with nonlinear distortions

Identification of Linear Systems with Nonlinear Distortions

Identification of Linear Systems with Nonlinear Distortions

Three free data sets for development and benchmarking in nonlinear system identification