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Perspectives on system identification
 In Plenary talk at the proceedings of the 17th IFAC World Congress, Seoul, South Korea
, 2008
"... System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous ne ..."
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Cited by 77 (2 self)
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System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the “science ” side, i.e. basic principles and results and at pointing to open problem areas in the practical, “art”, side of how to approach and solve a real problem. 1.
OptimizationBased Design of PlantFriendly Input Signals Using Geometric Discrepancy Criteria
 14 th IFAC Symposium on System Identification (SYSID 2006
, 2006
"... Abstract: The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for datacentric estimation methods, where uniform coverage of the output statespace is c ..."
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Abstract: The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for datacentric estimation methods, where uniform coverage of the output statespace is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy (1980). The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has significant benefits compared to multisine signals that minimize crest factor.
ASYMPTOTIC PROPERTIES OF JUSTINTIME MODELS
"... The concept of JustinTime models has been introduced for models that are not estimated until they are really needed. The prediction is taken as a weighted average of neighboring points in the regressor space, such that an optimal bias/variance tradeoff is achieved. The asymptotic properties of t ..."
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Cited by 1 (1 self)
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The concept of JustinTime models has been introduced for models that are not estimated until they are really needed. The prediction is taken as a weighted average of neighboring points in the regressor space, such that an optimal bias/variance tradeoff is achieved. The asymptotic properties of the method are investigated, and are compared to the corresponding properties of related statistical nonparametric kernel methods. It is shown that the rate of convergence for JustinTime models at least is in the same order as traditional kernel estimators, and that better rates probably can be achieved.
UNPUBLISHED
, 2005
"... AIChE shall not be responsible for statements or opinions contained in papers or printed in publications. The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meani ..."
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Cited by 1 (0 self)
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AIChE shall not be responsible for statements or opinions contained in papers or printed in publications. The design of constrained, “plantfriendly ” multisine input signals that optimize a geometric discrepancy criterion arising from Weyl’s Theorem is examined in this paper. Such signals are meaningful for datacentric estimation methods, where uniform coverage of the output statespace is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy (1980). The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The solution involves very little user intervention (which enhances its practical usefulness) and has significant benefits compared to multisine signals that minimize crest factor. The effectiveness of data resulting from a Weyl criterionbased signal for ModelonDemand Model Predictive Control (a datacentric multivariable control algorithm) is demonstrated for the distillation column case study.
AUTOMATIC CONTROL
, 2009
"... Technical reports from the Automatic Control group in Linköping are available from ..."
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Technical reports from the Automatic Control group in Linköping are available from
QUO VADIS, BAYESIAN IDENTIFICATION?
"... The Bayesian identification of nonlinear, nonGaussian, nonstationary or nonparametric models is notoriously known as computerintensive and not solvable in a closed form. The paper outlines three major approaches to approximate Bayesian estimation, based on locally weighted smoothing of data, ite ..."
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The Bayesian identification of nonlinear, nonGaussian, nonstationary or nonparametric models is notoriously known as computerintensive and not solvable in a closed form. The paper outlines three major approaches to approximate Bayesian estimation, based on locally weighted smoothing of data, iterative and noniterative Monte Carlo simulation and direct approximation of an information “distance ” between the empirical and model distributions of data. The informationbased view of estimation is used throughout to give more insight into the methods and show their mutual relationship.