System identification—A survey

Åström, Kalle; Eykhoff, P.

doi:10.1016/0005-1098(71)90059-8

Cited by 1,323 publications

(244 citation statements)

References 64 publications

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“…The minimization in (7) is similar to the weighting for the input error case where H = G so that y(t) = Gu+Ge = G(u+e), that is, the error enters the system with the input [13].…”

Section: Inverse Identification Of Lti Systemsmentioning

confidence: 99%

Estimating models of inverse systems

Jung

Enqvist

2013

52nd IEEE Conference on Decision and Control

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Abstract-This paper considers the problem of how to estimate a model of the inverse of a system. The use of inverse systems can be found in many applications, such as feedforward control and power amplifier predistortion. The inverse model is here estimated with the purpose of using it in cascade with the system itself, as an inverter. A good inverse model in this setting would be one that, when used in series with the original system, reconstructs the original input. The goal here is to select suitable inputs, experimental conditions and loss functions to obtain a good input estimate. Both linear and nonlinear systems will be discussed.For nonlinear systems, one way to obtain a linearizing prefilter is by Hirschorn's algorithm. It is here shown how to extend this to the postdistortion case, and some formulations of how the pre-or postinverter could be estimated are also presented.

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“…The minimization in (7) is similar to the weighting for the input error case where H = G so that y(t) = Gu+Ge = G(u+e), that is, the error enters the system with the input [13].…”

Section: Inverse Identification Of Lti Systemsmentioning

confidence: 99%

Estimating models of inverse systems

Jung

Enqvist

2013

52nd IEEE Conference on Decision and Control

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“…For modelling a system, it is essential to evaluate precisely the relations between input and output data in a simple manner (Åström and Eykhoff, 1971). Given that neural network and genetic algorithm have the ability to model the complicated systems (Sanchez et al, 1997), many attempts have been made to introduce evolutionary methods (Farlow, 1984).…”

Section: Introductionmentioning

confidence: 99%

Developing and Multi-Objective Optimization of a Combined Energy Absorber Structure Using Polynomial Neural Networks and Evolutionary Algorithms

Najibi

Shojaeefard

Yeganeh

2016

Lat. Am. j. solids struct.

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In this study a newly developed thin-walled structure with the combination of circular and square sections is investigated in term of crashworthiness. The results of the experimental tests are utilized to validate the Abaqus/Explicit TM finite element simulations and analysis of the crush phenomenon. Three polynomial metamodels based on the evolved group method of data handling (GMDH) neural networks are employed to simply represent the specific energy absorption (SEA), the initial peak crushing load (P1) and the secondary peak crushing load (P2) with respect to the geometrical variables. The training and testing data are extracted from the finite element analysis. The modified genetic algorithm NSGA-II, is used in multi-objective optimisation of the specific energy absorption, primary and secondary peak crushing load according to the geometrical variables. Finally, in each optimisation process, the optimal section energy absorptions are compared with the results of the finite element analysis. The nearest to ideal point and TOPSIS optimisation methods are applied to choose the optimal points.

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“…linear regression models is a widely studied subject in identification (see [1], [2]) originating from the classical results in statistics in terms of the Akaike Information Criterion (AIC) [3] and the Bayesian Information Criterion (BIC) [4]. More recently, statistical regularization (shrinkage) methods have been developed like the Non-Negative Garrote (NNG) or the Least Absolute Shrinkage and Selection Operator (LASSO) [5]- [7], or the Ridge Regression and the Elastic Net methods [8].…”

Section: Introductionmentioning

confidence: 99%

Compressive System Identification in the Linear Time-Invariant framework

Tóth

Sanandaji

Poolla

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-Selection of an efficient model parametrization (model order, delay, etc.) has crucial importance in parametric system identification. It navigates a trade-off between representation capabilities of the model (structural bias) and effects of over-parametrization (variance increase of the estimates). There exists many approaches to this widely studied problem in terms of statistical regularization methods and information criteria. In this paper, an alternative ℓ1 regularization scheme is proposed for estimation of sparse linear-regression models based on recent results in compressive sensing. It is shown that the proposed scheme provides consistent estimation of sparse models in terms of the so-called oracle property, it is computationally attractive for large-scale over-parameterized models and it is applicable in case of small data sets, i.e., underdetermined estimation problems. The performance of the approach w.r.t. other regularization schemes is demonstrated in an extensive Monte Carlo study.

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System identification—A survey

Cited by 1,323 publications

References 64 publications

Estimating models of inverse systems

Estimating models of inverse systems

Developing and Multi-Objective Optimization of a Combined Energy Absorber Structure Using Polynomial Neural Networks and Evolutionary Algorithms

Compressive System Identification in the Linear Time-Invariant framework

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