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About 10,000 papers for “System identification”

System Identification

Birkhäuser Basel eBooks · 2006

AbstractSystem identification is concerned with the estimation of a system on the basis of observed data. This involves specification of the model structure, estimation of the unknown model parameters, and validation of the resulting model. Least squares and maximum likelihood methods are discussed, for stationary processes (without inputs) and for input-output systems.KeywordsAutoregressive ModelUnbiased EstimatorOrthogonality ConditionWhite Noise ProcessWell Linear Unbiased EstimatorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cited by 17

System Identification.

Yashika Arora (11529693), Pushpinder Walia (11529696), Mitsuhiro Hayashibe (5521457) et al. · Figshare · 2021

<p>System identification using experimental data (total hemoglobin changes from stimulated hemisphere) from 11 subjects (Sub). * subjects with >-0.5 Correlation Coefficient between Oxy-Hb & Dxy-Hb.</p> <p>(DOCX)</p>

Cited by 0Open access

System Identification

Fabien Lauer, Gérard Bloch · Lecture notes in control and information sciences · 2018

Cited by 7

System Identification

A. V. Balakrishnan · Lecture notes in economics and mathematical systems · 1973

Cited by 555

System Identification

Anders Hansson, Martin Andersen · 2023

System identification is about learning models for dynamical systems. We define a regression problem for learning/estimating the dynamical system, and specifically we define it as a maximum likelihood problem. We discuss in detail how the parameters can be estimated by solving a nonlinear least squares problem. Recurrent neural networks and temporal convolutional neural networks are shown to be generalizations of the linear dynamical models to nonlinear dynamical models. The chapter is finished off with a discussion on experiment design for system identification.

Cited by 1Open access

System Identification

J. Jaidev Vyas, Balamurugan Gopalsamy, Harshavardhan Joshi · SpringerBriefs in applied sciences and technology · 2018

Cited by 18

System Identification

2018

System identification has its roots in standard statistical techniques and many of the basic routines have direct interpretations as well known statistical methods such as least squares and maximum likelihood. The control community took an active part in the development and application of these basic techniques to dynamic systems right after the birth of “modern control theory” in the early 1960s. Maximum likelihood estimation was applied to difference equations (ARMAX models) by Åström and Bohlin (1965) and thereafter a wide range of estimation techniques and model parameterizations flourished. By now, the area is well matured with established and well-understood techniques. Industrial use and application of the techniques has become standard. See Ljung (2007) for a common software package.

Cited by 3

system identification

Guan Gui, Abolfazl Mehbodniya, Fumiyuki Adachi · 2015

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal-to-noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response sys-tems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with lp-LMS and l0-LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can fur-ther improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algo-rithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better

Cited by 0

System Identification

T. So ̈derstro ̈m, Petre Stoica, Rolf Johansson · Journal of Dynamic Systems Measurement and Control · 1993

This book on linear system identification contains twelve chapters and two appendices with Chapter 1 (Introduction) devoted to a general motivation by means of application examples from engineering and science.Chapter 2 (Introductory Examples) contains a presentation of four aspects or factors affecting identification, i.e., the system, the model structure, the identification method and the experimental condition.A traditional but not very clear distinction is here being made between nonparametric methods (transient analysis and correlation analysis) and parametric methods adapted to linear models.Problems of bias and consistency are discussed in a traditional manner and followed by experimental issues in the form of excitation and complications due to feedback control.Chapter 3 (Nonparametric Methods) deals with a short treatment covering transient analysis, frequency (response) analysis, correlation analysis, and spectral analysis.Chapter 4 (Linear Regression) with a presentation of the least-squares method and the normal equations including statistical and computational aspects as applied to estimation of system parameters.In Chapter 5 (Input Signals) is being characterized what is an appropriate input to a system under investigation.Spectral and statistical properties such as persistency of excitation are covered.Chapter 6 (Model Parametrizations) contains a classification of various types of discrete-time linear input-output models with a discussion on uniqueness and identifiability.Little attention is given to multivariable systems.Chapter 7 (Prediction Error Methods) contains a framework aiming to assess identification as a mathematical problem related to the Kolmogorov-Wiener approaches to linear prediction.Statistical properties of least-squares methods and the maximum-likelihood method are treated with some detail for Gaussian noise processes.This chapter appears to be the main chapter of the book.Chapter 8 (Instrumental Variable Methods) treats a number of modifications to the least-squares method.Several algorithms usually presented in their own right such as the Yule-Walker equations or the Levinson-Durbin algorithm are here considered in the framework of instrumental methods.Chapter 9 (Recursive Identification Methods) is introduced as a special computational organization of least-squares estimation and the correspondence with the Kalman filter is demonstrated.Several different recursive identification methods are presented.In Chapter 10 (Identification of Systems Operating in Closed Loop) is considered the problems appearing when the system input is generated by a feedback loop.

Cited by 122Open access

System identification

Srinathkumar, S. (National Aeronautical Lab., Bangalore (India). Flight Mechanics and Control Div.), Doherr, K.F. (Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e.V. (DLR), Braunschweig (Germany). Inst. fuer Flugmechanik) (comps.), Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e.V. (DLR), Koeln (Germany) · OpenGrey (Institut de l'Information Scientifique et Technique) · 1993

Modeling and system identification pay a very significant role in the present day analysis of complex dynamical systems. The information obtained from the application of these techniques can be routinely used for flight simulation, control and prediction of dynamical behaviour of systems. Worldwide there has been significant advancement in the field over the last decade. In a collaborative programme between the Institute of Flight Mechanics, DLR, Braunschweig, Germany and Flight Mechanics and Control Division, NAL, Bangalore, India a symposium on 'System Identification' was organised to commemorate a decade of fruitful cooperation in this field. The symposium focused on the analytical, experimental and application oriented development of parameter estimation techniques and other related topics. Emphasis has been put on the output error and filter error methods, frequency domain methods, unstable and nonlinear systems, modeling of flexible wing, performance optimization, dynamic wind tunnel sumulation, neural network and flight data management methods. (orig.)

Cited by 0