Mixture modeling is a popular technique for identifying unobserved subpopulations (e.g., components) within a data set, with Gaussian (normal) mixture modeling being the form most widely used. Generally, the parameter...
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Mixture modeling is a popular technique for identifying unobserved subpopulations (e.g., components) within a data set, with Gaussian (normal) mixture modeling being the form most widely used. Generally, the parameters of these Gaussian mixtures cannot be estimated in closed form, so estimates are typically obtained via an iterative process. The most common estimation procedure is maximum likelihood via the expectation-maximization (EM) algorithm. Like many approaches for identifying subpopulations, finite mixture modeling can suffer from locally optimal solutions, and the final parameter estimates are dependent on the initial starting values of the EM algorithm. Initial values have been shown to significantly impact the quality of the solution, and researchers have proposed several approaches for selecting the set of starting values. Five techniques for obtaining starting values that are implemented in popular software packages are compared. Their performances are assessed in terms of the following four measures: (1) the ability to find the best observed solution, (2) settling on a solution that classifies observations correctly, (3) the number of local solutions found by each technique, and (4) the speed at which the start values are obtained. On the basis of these results, a set of recommendations is provided to the user.
The most competitive heuristics for calculating the median string are those that use perturbation-based iterative algorithms. Given the complexity of this problem, which under many formulations is NP-hard, the computa...
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The most competitive heuristics for calculating the median string are those that use perturbation-based iterative algorithms. Given the complexity of this problem, which under many formulations is NP-hard, the computational cost involved in the exact solution is not affordable. In this work, the heuristic algorithms that solve this problem are addressed, emphasizing its initialization and the policy to order possible editing operations. Both factors have a significant weight in the solution of this problem. Initial string selection influences the algorithm's speed of convergence, as does the criterion chosen to select the modification to be made in each iteration of the algorithm. To obtain the initial string, we use the median of a subset of the original dataset;to obtain this subset, we employ the Half Space Proximal (HSP) test to the median of the dataset. This test provides sufficient diversity within the members of the subset while at the same time fulfilling the centrality criterion. Similarly, we provide an analysis of the stop condition of the algorithm, improving its performance without substantially damaging the quality of the solution. To analyze the results of our experiments, we computed the execution time of each proposed modification of the algorithms, the number of computed editing distances, and the quality of the solution obtained. With these experiments, we empirically validated our proposal.
In this work, the parameter identification for systems with scarce measurements is addressed. A linear plant is assumed and its output is assumed to be available only at sporadic instants of time and affected by noise...
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ISBN:
(纸本)9781424477463
In this work, the parameter identification for systems with scarce measurements is addressed. A linear plant is assumed and its output is assumed to be available only at sporadic instants of time and affected by noise measurement. The identification is carried out estimating the missing outputs in order to construct the regression vector needed by the parameter estimation algorithm and using the available output information not only to update the estimated parameter vector, but also to update the regression vector in order to fasten the convergence of the algorithm. The problem is addressed with an adaptive extended Kalman filter that estimates and correct both the parameters and the regression vector, allowing to improve the convergence speed of the algorithm with respect to other existing ones on the literature as it is shown with several examples.
In this paper, the problem of initializing identification algorithms with non-regular sampling is addressed. When a recursive identification algorithm is used to estimate the parameters, the convergence of the paramet...
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In this paper, the problem of initializing identification algorithms with non-regular sampling is addressed. When a recursive identification algorithm is used to estimate the parameters, the convergence of the parameters is affected by the existence of wrong attractors. The initialization of the algorithm is studied in different situations. First, the algorithm starts without past information about the model parameters. An interpolation method is used to estimate the missing data. If a change of the control action updating rate is planned, the new model parameters are initialized by estimations obtained either by interpolation (if the periods are multiple) or by approximate δ modelling using the measurements taken under current operating conditions. Some examples illustrate the attractors avoidance and some conclusions are drafted.
In this work, the parameter identification for systems with scarce measurements is addressed. A linear plant is assumed and its output is assumed to be available only at sporadic instants of time and affected by noise...
详细信息
ISBN:
(纸本)9781424477456
In this work, the parameter identification for systems with scarce measurements is addressed. A linear plant is assumed and its output is assumed to be available only at sporadic instants of time and affected by noise measurement. The identification is carried out estimating the missing outputs in order to construct the regression vector needed by the parameter estimation algorithm and using the available output information not only to update the estimated parameter vector, but also to update the regression vector in order to fasten the convergence of the algorithm. The problem is addressed with an adaptive extended Kalman filter that estimates and correct both the parameters and the regression vector, allowing to improve the convergence speed of the algorithm with respect to other existing ones on the literature as it is shown with several examples.
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