This paper focuses on the robust parameters estimation algorithm of linear parameters varying (LPV) models. The classical robust identification techniques deal with the polluted training data, for example, outliers in...
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This paper focuses on the robust parameters estimation algorithm of linear parameters varying (LPV) models. The classical robust identification techniques deal with the polluted training data, for example, outliers in white noise. The paper extends this robustness to both symmetric and asymmetric noise with outliers to achieve stronger robustness. Without the assumption of Gaussian white noise pollution, the paper employs asymmetric Laplace distribution to model broader noise, especially the asymmetrically distributed noise, since it is an asymmetric heavy-tailed distribution. Furthermore, the asymmetric Laplace (AL) distribution is represented as the product of Gaussian distribution and exponential distribution to decompose this complex AL distribution. Then, a shifted parameter is introduced as the regression term to connect the probabilistic models of the noise and the predict output that obeys shifted AL distribution. In this way, the posterior probability distribution of the unobserved variables could be deduced and the robust parameters estimation problem is solved in the general expectation maximization algorithm framework. To demonstrate the advantage of the proposed algorithm, a numerical simulation example is employed to identify the parameters of LPV models and to illustrate the convergence.
作者：
Kubota, TakuyaAritsugi, MasayoshiKumamoto Univ
Grad Sch Sci & Technol Comp Sci & Elect Engn Chuo Ku 2-39-1 Kurokami Kumamoto 8608555 Japan Kumamoto Univ
Fac Adv Sci & Technol Div Environm Sci Big Data Sci & TechnolChuo Ku 2-39-1 Kurokami Kumamoto 8608555 Japan
It is expected that ground truths can result in many good labels in the crowdsourcing of labeling tasks. However, the use of ground truths has so far not been adequately addressed. In this paper, we develop algorithms...
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It is expected that ground truths can result in many good labels in the crowdsourcing of labeling tasks. However, the use of ground truths has so far not been adequately addressed. In this paper, we develop algorithms that determine the number of ground truths that are necessary. We determine this number by iteratively calculating the expected quality of labels for tasks with various sets of ground truths, and then comparing the quality with the limit of the estimated label quality expected to be obtained by crowd sourcing. We assume that each worker has a different unknown labeling ability and performs a different number of tasks. Under this assumption, we develop assignment strategies for ground truths based on the estimated confidence intervals of the workers. Our algorithms can utilize different approaches based on the expectationmaximization to estimate good-quality consensus labels. An experimental evaluation demonstrates that our algorithms work well in various situations. (C) 2016 Elsevier Inc. All rights reserved.
The performance of several regression methods is investigated to estimate the distribution of engineering demand parameters conditioned on intensity measures (EDP|IM) for small record sets. In particular, the performa...
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The performance of several regression methods is investigated to estimate the distribution of engineering demand parameters conditioned on intensity measures (EDP|IM) for small record sets. In particular, the performance of the multivariate ordinary least squares (OLS), a simultaneous mean-variance regression (MVR) done by a penalized weighted least-square loss function, and a mean-covariance/variance regression based on expectationmaximization method (EM) are assessed. The efficiency of the introduced methods is compared with FEMA-P58 methodology. Performance assessment of EM and MVR methods shows that the overall increase in efficiency is about 25-45% for maximum inter-story drift ratios, and 30-50% for maximum absolute floor acceleration.
A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the param...
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A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the parameters. It accepted a set of 3D points with weights, corresponding to voxel or atomic centers. Although the standard algorithm worked reasonably well;however, it had three problems. First, it ignored the size (voxel width or atomic radius) of the input, and thus it could lead to a GMM with a smaller spread than the input. Second, the algorithm had a singularity problem, as it sometimes stopped the iterative procedure due to a Gaussian function with almost zero variance. Third, a map with a large number of voxels required a long computation time for conversion to a GMM. To solve these problems, we have introduced a Gaussian-input GMM algorithm, which considers the input atoms or voxels as a set of Gaussian functions. The standard EM algorithm of GMM was extended to optimize the new GMM. The new GMM has identical radius of gyration to the input, and does not suddenly stop due to the singularity problem. For fast computation, we have introduced a downsampled Gaussian functions (DSG) by merging neighboring voxels into an anisotropic Gaussian function. It provides a GMM with thousands of Gaussian functions in a short computation time. We also have introduced a DSG-input GMM: the Gaussian-input GMM with the DSG as the input. This new algorithm is much faster than the standard algorithm.
In this paper, we investigate the performance of the maximum likelihood (ML) method to estimate the parameters of compound Nakagami-gamma shadowed fading channels. We derived analytically the ML estimates of the Nakag...
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In this paper, we investigate the performance of the maximum likelihood (ML) method to estimate the parameters of compound Nakagami-gamma shadowed fading channels. We derived analytically the ML estimates of the Nakagami-gamma distribution by using the expectation-maximization (EM) algorithm. Numerical simulations demonstrate the outperformance of the EM algorithm in terms of accuracy versus the moment-based estimation technique. Moreover, the EM iterations converge to the global optimum.
Cross-directional control and monitoring of paper machines require a knowledge of cross directional profile of the quality variables. However, sensors used in paper machines follow a zig-zag trajectory providing only ...
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Cross-directional control and monitoring of paper machines require a knowledge of cross directional profile of the quality variables. However, sensors used in paper machines follow a zig-zag trajectory providing only data that is a combination of cross and machine direction variations. In this paper, we propose a new model-based approach to estimate the complete cross directional profile. This method is based on a modification of expectationmaximization approach for a new model proposed in this paper. In a typical paper machine, the percentage of missing data is of the order of 99%. The proposed model reduces the missing data to about 50% and thus increases the reliability of the estimated model. Moreover, the proposed method ensures linear space invariance and symmetry of the cross directional response of the model with the added flexibility of using different models near the edges. The results are verified through simulations.
An unsupervised retraining technique for a maximum likelihood (ML) classifier is presented. The proposed technique allows the classifier's parameters, obtained by supervised learning on a specific image, to be upd...
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An unsupervised retraining technique for a maximum likelihood (ML) classifier is presented. The proposed technique allows the classifier's parameters, obtained by supervised learning on a specific image, to be updated in a totally unsupervised way on the basis of the distribution of a new image to be classified. This enables the classifier to provide a high accuracy for the new image even when the corresponding training set is not available.
We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the ex...
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Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove difficult in some sett...
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Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. However, their reliance on large-scale c...
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