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| 2. |
- Ohlsson, Henrik, 1981-, et al.
(author)
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Direct Weight Optimization Applied to Discontinuous Functions
- 2008
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In: 47th IEEE Conference on Decision and Control, 2008. CDC 2008. - Cancun, Mexico : IEEE. - 9781424431236 ; , s. 117-122
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Conference paper (peer-reviewed)abstract
- The Direct Weight Optimization (DWO) approach is a nonparametric estimation approach that has appeared in recent years within the field of nonlinear system identification. In previous work, all function classes for which DWO has been studied have included only continuous functions. However, in many applications it would be desirable also to be able to handle discontinuous functions. Inspired by the bilateral filter method from image processing, such an extension of the DWO framework is proposed for the smoothing problem. Examples show that the properties of the new approach regarding the handling of discontinuities are similar to the bilateral filter, while at the same time DWO offers a greater flexibility with respect to different function classes handled.
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| 3. |
- Gumussoy, Suat, et al.
(author)
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Improving Linear State-Space Models with Additional Iterations
- 2018
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In: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 51:15, s. 341-346
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Conference paper (peer-reviewed)abstract
- An estimated state-space model can possibly be improved by further iterations with estimation data. This contribution specifically studies if models obtained by subspace estimation can be improved by subsequent re-estimation of the B, C, and D matrices (which involves linear estimation problems). Several tests are performed, which show that it is generally advisable to do such further re-estimation steps using the maximum likelihood criterion. Stated more succinctly in terms of MATLAB® functions, ssest generally outperforms n4sid.
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| 4. |
- Fredriksson, Odd, et al.
(author)
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Critical Success Conditions for Enterprise Systems Change Projects
- 2010
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Conference paper (peer-reviewed)abstract
- Enterprise systems are configurable and customized computer-based information systems, claiming to provide a total, integrated solution to firms' information-processing needs. 'Management support', 'User Involvement', and 'Project Management Experience' are considered as three of the most important critical success conditions for Enterprise systems change projects. The main purpose of this paper is to identify and discuss new preconditions for established critical success conditions in Enterprise systems change projects. Traditional Enterprise systems technology and traditional plan-driven project management methods are not in an effective way supporting the established critical success conditions for Enterprise systems change projects. Supported by the findings from one case study, we propose that new preconditions, such as modern Enterprise systems technology, more narrowly defined projects and agile methods are matching each other. We also propose that this combination of new preconditions is more appropriate for supporting the realization of critical success conditions for Enterprise systems change projects
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| 5. |
- Fredriksson, Odd, et al.
(author)
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Modern Enterprise Systems as Enablers of Agile Development
- 2010
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Conference paper (peer-reviewed)abstract
- Traditional ES technology and traditional project management methods are supporting and matching each other. But they are not in an effective way sup-porting the critical success conditions for ES development. Although the findings from one case study of a successful modern ES change project is not strong empirical evidence, we carefully propose that the new modern ES technology is sup-porting and matching agile project management methods. In other words, it pro-vides the required flexibility which makes it possible to put into practice the agile way of running projects, both for the system supplier and for the customer. In addition, we propose that the combination of modern ES technology and agile project management methods are more appropriate for supporting the realization of critical success conditions for ES development. The main purpose of this paper is to compare critical success conditions for modern enterprise systems development projects with critical success conditions for agile information systems development projects
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| 6. |
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| 7. |
- Jansson, Tomas, et al.
(author)
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New times, new projects, new tools required
- 2010
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Conference paper (pop. science, debate, etc.)abstract
- Today, it is almost impossible to find organisations without any ongoing projects. Projects seem to play an increasingly important role in most organisations. Projects are nowadays often small, frequent and a normal way of organising work. Most project managers are working in arenas quite different compared with those where the standard toolbox was originally developed, i.e. large construction and development projects. During the last two decades, several researchers have stressed the necessity of adapting leadership style and usage of techniques, e.g. for project planning, to meet the specific requirements of each project type. In this paper we move one step further, and suggest that the choice of project management methods and techniques must be based on the characteristics of each individual project. If there would be only one common tool in the toolbox for every project manager it ought to be an analysis model or method to create an understanding of the actual projects characteristics. Supported by three comprehensive case studies in three different organisations, carried out through an action research methodology during 5 years (2004-2009), this paper presents two models for such project analysis. The first model is a suggested project typology consisting of five project archetypes derived from a strategic management perspective. The second is a project diagnosis, featuring 15 questions formulated to reveal important project characteristics with relevance for the required leadership. We argue that these two models can serve as useful tools for project management and business oriented steering, since they are aiming at guiding leaders towards effectively focusing their attention to the areas most critical for project success
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| 8. |
- Ju, Yue, et al.
(author)
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Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyperparameter Estimator
- 2023
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In: IEEE Transactions on Automatic Control. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9286 .- 1558-2523. ; 68:12, s. 7224-7239
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Journal article (peer-reviewed)abstract
- Regularized techniques, also named as kernel-based techniques, are the major advances in system identification in the last decade. Although many promising results have been achieved, their theoretical analysis is far from complete and there are still many key problems to be solved. One of them is the asymptotic theory, which is about convergence properties of the model estimators as the sample size goes to infinity. The existing related results for regularized system identification are about the almost sure convergence of various hyperparameter estimators. A common problem of those results is that they do not contain information on the factors that affect the convergence properties of those hyperparameter estimators, e.g., the regression matrix. In this article, we tackle problems of this kind for the regularized finite impulse response model estimation with the empirical Bayes (EB) hyperparameter estimator and filtered white noise input. In order to expose and find those factors, we study the convergence in distribution of the EB hyperparameter estimator, and the asymptotic distribution of its corresponding model estimator. For illustration, we run Monte Carlo simulations to show the efficacy of our obtained theoretical results.
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| 9. |
- Ju, Yue, et al.
(author)
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On Convergence in Distribution of Steins Unbiased Risk Hyper-parameter Estimator for Regularized System Identification
- 2022
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In: 2022 41ST CHINESE CONTROL CONFERENCE (CCC). - : IEEE. - 9789887581536 - 9781665482561 ; , s. 1491-1496
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Conference paper (peer-reviewed)abstract
- Asymptotic theory for the regularized system identification has received increasing interests in recent years. In this paper, for the finite impulse response (FIR) model and filtered white noise inputs, we show the convergence in distribution of the Steins unbiased risk estimator (SURE) based hyper-parameter estimator and find factors that influence its convergence properties. In particular, we consider the ridge regression case to obtain closed-form expressions of the limit of the regression matrix and the variance of the limiting distribution of the SURE based hyper-parameter estimator, and then demonstrate their relation numerically.
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| 10. |
- Lindsten, Fredrik, 1984-
(author)
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Particle filters and Markov chains for learning of dynamical systems
- 2013
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Doctoral thesis (other academic/artistic)abstract
- Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods.Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forward-only fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of non-Markovian latent variable models.Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known Rao-Blackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by Rao-Blackwellization. Furthermore, a Rao-Blackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear state-space models. The idea of Rao-Blackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear state-space models with affine parameter dependencies.
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