| 1. |
|
|
| 2. |
- Gustavsson, Ivar, et al.
(författare)
-
Identification of Processes in Closed Loop-Identifiability and Accuracy Aspects
- 1977
-
Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 13, s. 59-75
-
Tidskriftsartikel (refereegranskat)abstract
- It is often necessary in practice to perform identification experiments on systems operating in closed loop. There has been some confusion about the possibilities of successful identification in such cases, evidently due to the fact that certain common methods then fail. A rapidly increasing literature on the problem is briefly surveyed in this paper, and an overview of a particular approach is given. It is shown that prediction error identification methods, applied in a direct fashion will given correct estimates in a number of feedback cases. Furthermore, the accuracy is not necessarily worse in the presence of feedback; in fact optimal inputs may very well require feedback terms. Some practical applications are also described.
|
|
| 3. |
- Söderström, Torsten, et al.
(författare)
-
A Theoretical Analysis of Recursive Identification
- 1978
-
Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 14:231-244
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper five different recursive identification methods will be analyzed and compared, namely recursive versions of the least squares method, the instrumental variable method, the generalized least squares method, the extended least squares method and the maximum likelihood method. They are shown to be similar in structure and need of computer storage and time. Making use of recently developed theory for asymptotic analysis of recursive stochastic algorithms, these methods are examined from a theoretical viewpoint. Possible convergence points and their global and local convergence properties are studied. The theoretical analysis is illustrated and supplemented by simulations.
|
|
| 4. |
- Wahlberg, Bo, 1959-, et al.
(författare)
-
On Sampling of Continuous Time Stochastic Processes
- 1993
-
Ingår i: Control, Theory and Advanced Technology. - 0911-0704. ; 9:1, s. 99-112
-
Tidskriftsartikel (refereegranskat)abstract
- Techniques for sampling of continuous time stochastic processes are presented. To obtain flexible models and well-posed filtering problems, we assume an underlying continuous time innovations model. To sample such a model `averaged sampling' is applied. It is shown that this technique is equivalent to the following two step procedure: Determine by instantaneous (direct) sampling a discrete model for the continuous time process obtained by integrating the original innovations model. Then differentiate the sampled process to remove the discrete pole at z = 1 introduced by the integration. An advantage with this procedure is that one obtains ARMA(n, n) models, while instantaneous sampling only gives ARMA(n, n-1) models. Furthermore, the problem of updating discrete time models, without using a continuous time model, in case of a change of sampling rate - decimation/interpolation - is addressed.
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
- Lind, Ingela, 1975-
(författare)
-
Regressor and Structure Selection : Uses of ANOVA in System Identification
- 2006
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Identification of nonlinear dynamical models of a black box nature involves both structure decisions (i.e., which regressors to use and the selection of a regressor function), and the estimation of the parameters involved. The typical approach in system identification is often a mix of all these steps, which for example means that the selection of regressors is based on the fits that is achieved for different choices. Alternatively one could then interpret the regressor selection as based on hypothesis tests (F-tests) at a certain confidence level that depends on the data. It would in many cases be desirable to decide which regressors to use, independently of the other steps. A survey of regressor selection methods used for linear regression and nonlinear identification problems is given.In this thesis we investigate what the well known method of analysis of variance (ANOVA) can offer for this problem. System identification applications violate many of the ideal conditions for which ANOVA was designed and we study how the method performs under such non-ideal conditions. It turns out that ANOVA gives better and more homogeneous results compared to several other regressor selection methods. Some practical aspects are discussed, especially how to categorise the data set for the use of ANOVA, and whether to balance the data set used for structure identification or not.An ANOVA-based method, Test of Interactions using Layout for Intermixed ANOVA (TILIA), for regressor selection in typical system identification problems with many candidate regressors is developed and tested with good performance on a variety of simulated and measured data sets.Typical system identification applications of ANOVA, such as guiding the choice of linear terms in the regression vector and the choice of regime variables in local linear models, are investigated.It is also shown that the ANOVA problem can be recast as an optimisation problem. Two modified, convex versions of the ANOVA optimisation problem are then proposed, and it turns out that they are closely related to the nn-garrote and wavelet shrinkage methods, respectively. In the case of balanced data, it is also shown that the methods have a nice orthogonality property in the sense that different groups of parameters can be computed independently.
|
|
| 9. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
Theory and Practice of Recursive Identification
- 1983
-
Bok (övrigt vetenskapligt/konstnärligt)abstract
- Methods of recursive identification deal with the problem of building mathematical models of signals and systems on-line, at the same time as data is being collected. Such methods, which are also known as adaptive algorithms or sequential parameter estimation methods, may be applied to a wide spectrum of online adaptive systems, such as devices for signal processing, prediction, or control and are useful for modeling systems in general. For example, they can be used to analyze the demand for power on an electric generating grid and help the grid adjust to continually changing power needs, or applied to the changing conditions of a papermaking plant, or to monitoring pollution in a river.This book provides a comprehensive and systematic framework for developing, describing, and analyzing such recursive algorithms. It has been carefully designed and organized to meet the needs of readers with different objectives. With a myriad of algorithms now in use, it provides a simple and coherent frame of reference for understanding the subject and will serve as a guide to the large number of choices made available by the advent of inexpensive, powerful digital processors.Readers primarily interested in theory will find a detailed development of convergence analysis and asymptotic distribution results. For graduate students it is a basic introduction to the subject. And for engineers interested in practical applications, the book's earlier theory-oriented chapters are equipped with "user's summaries" that provide direct access to the discussion of practical aspects developed in the final three chapters on implementation and applications.
|
|
| 10. |
- Söderström, Torsten, et al.
(författare)
-
Analysis of some On-Line Identification Methods
- 1976
-
Ingår i: Proceedings of the 4th IFAC Symposium on Identification and System Parameter Estimation. - 9780444851130
-
Konferensbidrag (refereegranskat)
|
|
