| 1. |
- Björklund, Svante, et al.
(författare)
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An Improved Phase Method for Time-Delay Estimation
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A promising method for estimation of the time-delay in continuous-time linear dynamical systems uses the phase of the all-pass part of a discrete-time model of the system. We have discovered that this method can sometimes fail totally and we suggest a method for avoiding such failures.
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| 2. |
- Cassasco, Diego S., et al.
(författare)
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On the Accuracy of Parameter Estimation for Continuous Time Nonlinear Systems from Sampled Data
- 2011
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Ingår i: Proceedings of the 50th IEEE Conference on Decision and Control. - 9781612847993 - 9781612848006 ; , s. 4308-4311
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Konferensbidrag (refereegranskat)abstract
- This paper deals with the issue of estimating the parameters in a continuous-time nonlinear dynamical model from sampled data. We focus on the issue of bias-variance trade-offs. In particular, we show that the bias error can be significantly reduced by using a particular form of sampled data model based on truncated Taylor series. This model retains the conceptual simplicity of models based on Euler integration but has much improved accuracy as a function of the sampled period.
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| 3. |
- Chen, Tianshi, et al.
(författare)
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Decentralization of Particle Filters Using Arbitrary State Decomposition
- 2010
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Ingår i: Proceedings of the 49th IEEE Conference on Decision and Control. - 9781424477456 ; , s. 7383-7388
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Konferensbidrag (refereegranskat)abstract
- In this paper, a new particle filter (PF) which we refer to as the decentralized PF (DPF) is proposed. By first decomposing the state into two parts, the DPF splits the filtering problem into two nested sub-problems and then handles the two nested sub-problems using PFs. The DPF has an advantage over the regular PF that the DPF can increase the level of parallelism of the PF. In particular, part of the resampling in the DPF bears a parallel structure and thus can be implemented in parallel. The parallel structure of the DPF is created by decomposing the state space, differing from the parallel structure of the distributed PFs which is created by dividing the sample space. This difference results in a couple of unique features of the DPF in contrast with the existing distributed PFs. Simulation results from a numerical example indicates that the DPF has a potential to achieve the same level of performance as the regular PF, in a shorter execution time.
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| 4. |
- Chen, Tianshi, et al.
(författare)
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Decentralized Particle Filter with Arbitrary State Decomposition
- 2011
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Ingår i: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 59:2, s. 465-478
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a new particle filter (PF) which we refer to as the decentralized PF (DPF) is proposed. By first decomposing the state into two parts, the DPF splits the filtering problem into two nested subproblems and then handles the two nested subproblems using PFs. The DPF has the advantage over the regular PF that the DPF can increase the level of parallelism of the PF. In particular, part of the resampling in the DPF bears a parallel structure and can thus be implemented in parallel. The parallel structure of the DPF is created by decomposing the state space, differing from the parallel structure of the distributed PFs which is created by dividing the sample space. This difference results in a couple of unique features of the DPF in contrast with the existing distributed PFs. Simulation results of two examples indicate that the DPF has a potential to achieve in a shorter execution time the same level of performance as the regular PF.
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| 5. |
- Chen, Tianshi, et al.
(författare)
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Impulse Response Estimation with Binary Measurements : A Regularized FIR Model
- 2012
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Ingår i: Proceedings of the 16th IFAC Symposium on System Identification. - 9783902823069 ; , s. 113-118
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Konferensbidrag (refereegranskat)abstract
- FIR (finite impulse response) model is widely used in tackling the problem of the impulse response estimation with quantized measurements. Its use is, however, limited, in the case when a high order FIR model is required to capture a slowly decaying impulse response. This is because the high variance for high order FIR models would override the low bias and thus lead to large MSE (mean square error). In this contribution, we apply the recently introduced regularized FIR model approach to the problem of the impulse response estimation with binary measurements. We show by Monte Carlo simulations that the proposed approach can yield both better accuracy and better robustness than a recently introduced FIR model based approach.
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| 6. |
- Chen, Tianshi, et al.
(författare)
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Kernel Selection in Linear System Identification : Part II: A Classical Perspective
- 2011
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Ingår i: Proceedings of the 50th IEEE Conference on Decision and Control. - 9781612847993 - 9781612848006 ; , s. 4326-4331
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Konferensbidrag (refereegranskat)abstract
- In this companion paper, the choice of kernels for estimating the impulse response of linear stable systems is considered from a classical, “frequentist”, point of view. The kernel determines the regularization matrix in a regularized least squares estimate of an FIR model. The quality is assessed from a mean square error (MSE) perspective, and measures and algorithms for optimizing the MSE are discussed. The ideas are tested on the same data bank as used in Part I of the companion papers. The resulting findings and conclusions in the two papers are very similar despite the different perspectives.
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| 7. |
- Chen, Tianshi, et al.
(författare)
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On the Estimation of Transfer Functions, Regularizations and Gaussian Processes – Revisited
- 2010
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Ingår i: Proceedings of the 18th IFAC World Congress. - 9783902661937 ; , s. 2303-2308
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Konferensbidrag (refereegranskat)abstract
- Intrigued by some recent results on impulse response estimation by kernel and nonparametric techniques, we revisit the old problem of transfer function estimation from input-output measurements.We formulate a classical regularization approach, focused on finite impulse response (FIR) models, and find that regularization is necessary to cope with the high variance problem. This basic, regularized least squares approach is then a focal point for interpreting other techniques, like Bayesian inference and Gaussian process regression.
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| 8. |
- Chen, Tianshi, et al.
(författare)
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On the Estimation of Transfer Functions, Regularizations and Gaussian Processes - Revisited
- 2012
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 48:8, s. 1525-1535
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Tidskriftsartikel (refereegranskat)abstract
- Intrigued by some recent results on impulse response estimation by kernel and nonparametric techniques, we revisit the old problem of transfer function estimation from input-output measurements. We formulate a classical regularization approach, focused on finite impulse response (FIR) models, and find that regularization is necessary to cope with the high variance problem. This basic, regularized least squares approach is then a focal point for interpreting other techniques, like Bayesian inference and Gaussian process regression. The main issue is how to determine a suitable regularization matrix (Bayesian prior or kernel). Several regularization matrices are provided and numerically evaluated on a data bank of test systems and data sets. Our findings based on the data bank are as follows. The classical regularization approach with carefully chosen regularization matrices shows slightly better accuracy and clearly better robustness in estimating the impulse response than the standard approach - the prediction error method/maximum likelihood (PEM/ML) approach. If the goal is to estimate a model of given order as well as possible, a low order model is often better estimated by the PEM/ML approach, and a higher order model is often better estimated by model reduction on a high order regularized FIR model estimated with careful regularization. Moreover, an optimal regularization matrix that minimizes the mean square error matrix is derived and studied. The importance of this result lies in that it gives the theoretical upper bound on the accuracy that can be achieved for this classical regularization approach.
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| 9. |
- Falck, Tillmann, et al.
(författare)
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Segmentation of Time Series from Nonlinear Dynamical Systems
- 2011
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Ingår i: Proceedings of the 18th IFAC World Congress. - 9783902661937 ; , s. 13209-13214
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Konferensbidrag (refereegranskat)abstract
- Segmentation of time series data is of interest in many applications, as for example in change detection and fault detection. In the area of convex optimization, the sum-of-norms regularization has recently proven useful for segmentation. Proposed formulations handle linear models, like ARX models, but cannot handle nonlinear models. To handle nonlinear dynamics, we propose integrating the sum-of-norms regularization with a least squares support vector machine (LS-SVM) core model. The proposed formulation takes the form of a convex optimization problem with the regularization constant trading off the fit and the number of segments.
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| 10. |
- Gillberg, Jonas, et al.
(författare)
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Frequency-Domain Identification of Continuous-Time ARMA Models from Sampled Data
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The subject of this paper is the direct identification of continuous-time autoregressive moving average (CARMA) models. The topic is viewed from the frequency domain perspective which then turns the reconstruction of the continuous-time power spectral density (CT-PSD) into a key issue. The first part of the paper therefore concerns the approximate estimation of the CT-PSD from uniformly sampled data under the assumption that the model has a certain relative degree. The approach has its point of origin in the frequency domain Whittle likelihood estimator. The discrete- or continuous-time spectral densities are estimated from equidistant samples of the output. For low sampling rates the discrete-time spectral density is modeled directly by its continuous-time spectral density using the Poisson summation formula. In the case of rapid sampling the continuous-time spectral density is estimated directly by modifying its discrete-time counterpart.
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