| 1. |
- Aljanaideh, Khaled F., et al.
(author)
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New Features in the System Identification Toolbox - Rapprochements with Machine Learning
- 2021
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In: IFAC PAPERSONLINE. - : ELSEVIER. - 2405-8963. ; , s. 369-373
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Conference paper (peer-reviewed)abstract
- The R2021b release of the System Identification ToolboxTM for MATLAB contains new features that enable the use of machine learning techniques for nonlinear system identification. With this release it is possible to build nonlinear ARX models with regression tree ensemble and Gaussian process regression mapping functions. The release contains several other enhancements including, but not limited to, (a) online state estimation using the extended Kalman filter and the unscented Kalman filter with code generation capability; (b) improved handling of initial conditions for transfer functions and polynomial models; (c) a new architecture of nonlinear black-box models that streamlines regressor handling, reduces memory footprint and improves numerical accuracy; and (d) easy incorporation of identification apps in teaching tools and interactive examples by leveraging the Live Editor tasks of MATLAB. Copyright (C) 2021 The Authors.
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| 2. |
- Andersson, Torbjörn, et al.
(author)
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Identification Aspects of Inter-Sample Input Behavior
- 1994
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In: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 137-142
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Conference paper (peer-reviewed)abstract
- In this contribution aspects of inter-sample input signal behavior are examined. The starting point is that parametric identification always is performed on basis of discrete-time data. This is valid for identification of discrete-time models as well as continuous-time models. The usual assumptions on the input signal are; i) it is band-limited, ii) it is piecewise constant or iii) it is piecewise linear. One point made in this paper is that if a discrete-time model is used, the best possible (in the model structure) adjustment to data is made. This is independent of the assumption on the input signal. However, a transformation of the obtained discrete model to a continuous one is not possible without additional assumptions on the input signal. The other point made is that the frequency functions of the discrete models very well coincides with the frequency functions of the discretized continuous time models and the continuous time transfer function fitted in the frequency domain.
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| 3. |
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| 4. |
- Caines, Peter E., et al.
(author)
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Prediction Error Estimators : Asymptotic Normality and Accuracy
- 1976
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In: Proceedings of the 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes. ; , s. 652-658
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Conference paper (peer-reviewed)abstract
- In this paper the asymptotic normality of a large class of prediction error estimators is established. (Prediction error identification methods were introduced in [1] and further developed in [2] and [3].) The observed processes in this paper are assumed to be stationary and ergodic and the parameterized process models are taken to be non-linear regression models. In the gaussian case the results presented in this paper constitute substantial generalizations of previous results concerning the asymptotic normality of maximum likelihood estimators for (i) processes of independent random variables [9,4] and (ii) Markov processes [5]; these results also generalize previous results on the asymptotic normality of least squares estimators for autoregressive moving average processes [6,7]. The asymptotic normality theorem gives formulae for the covariances of the asymptotic distributions of the parameter estimation errors arising from the specified class of prediction error identification methods. Employing these formulae it is demonstrated that the prediction error method using the determinant of the residual error covariance matrix as loss function is asymptotically efficient with respect to the specified class of prediction error estimators regardless of the distribution of the observed processes.
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| 5. |
- Cassasco, Diego S., et al.
(author)
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On the Accuracy of Parameter Estimation for Continuous Time Nonlinear Systems from Sampled Data
- 2011
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In: Proceedings of the 50th IEEE Conference on Decision and Control. - 9781612847993 - 9781612848006 ; , s. 4308-4311
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Conference paper (peer-reviewed)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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| 6. |
- Chen, Tianshi, et al.
(author)
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Decentralization of Particle Filters Using Arbitrary State Decomposition
- 2010
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In: Proceedings of the 49th IEEE Conference on Decision and Control. - 9781424477456 ; , s. 7383-7388
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Conference paper (peer-reviewed)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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| 7. |
- Chen, Tianshi, et al.
(author)
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Impulse Response Estimation with Binary Measurements : A Regularized FIR Model
- 2012
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In: Proceedings of the 16th IFAC Symposium on System Identification. - 9783902823069 ; , s. 113-118
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Conference paper (peer-reviewed)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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| 8. |
- Chen, Tianshi, et al.
(author)
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Kernel Selection in Linear System Identification : Part II: A Classical Perspective
- 2011
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In: Proceedings of the 50th IEEE Conference on Decision and Control. - 9781612847993 - 9781612848006 ; , s. 4326-4331
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Conference paper (peer-reviewed)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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| 9. |
- Chen, Tianshi, et al.
(author)
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On the Estimation of Transfer Functions, Regularizations and Gaussian Processes – Revisited
- 2010
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In: Proceedings of the 18th IFAC World Congress. - 9783902661937 ; , s. 2303-2308
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Conference paper (peer-reviewed)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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| 10. |
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