Journal Article10.1115/1.1409552
An Algorithm for Bayes Parameter Identification
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TL;DR: In this article, the authors deal with the task of parameter identification using the Bayes estimation method, which makes it possible to take into account the differing consequences of positive and negative estimation errors.
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Abstract: This paper deals with the task of parameter identification using the Bayes estimation method, which makes it possible to take into account the differing consequences of positive and negative estimation errors The calculation procedures are based on the kernel estimators technique The final result constitutes a complete algorithm usable for obtaining the value of the Bayes estimator on the basis of an experimentally obtained random sample An elaborated method is provided for numerical computations
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Citations
Kernel Estimators in Industrial Applications
Piotr Kulczycki
- 01 Jan 2008
TL;DR: Kernel estimators are becoming the principal method in this subject, although their concept is relatively simple and interpretation transparent, the applications are impossible without a high class of computer which, even until recently, significantly hindered theoretical, and especially practical research.
Fuzzy controller for a system with uncertain load
Piotr Kulczycki,Rafal Wisniewski +1 more
TL;DR: A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load, and may be easily adapted to other modeling uncertainties of mechanical systems, e.g. parameters of drive or motion resistance.
17
•Journal Article
Statistical inference for fault detection: A complete algorithm based on Kernel estimators
TL;DR: A new concept for a statistical fault detection system, including the detection, diagnosis, and prediction of faults, is presented, which can be used in a broad range of applications, including those outside the scope of engineering.
15
An algorithm for conditional multidimensional parameter identification with asymmetric and correlated losses of under- and overestimations
TL;DR: In this article, the authors considered the problem of the estimation of a vector of parameters, where losses resulting from their under- and overestimation are asymmetric and mutually correlated, from a supplementary conditional aspect, where particular coordinates of conditioning variables may be continuous, discrete, multivalued, or categorized (ordered and unordered).
10
Nonparametric estimation for control engineering
Piotr Kulczycki
- 26 Oct 2008
TL;DR: A detailed description of the Bayes parameter estimation with asymmetrical polynomial loss function will be given, as will one for fault detection in dynamical systems as objects of automatic control, in the scope of detection, diagnosis and prognosis of malfunctions.
9
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Bernard W. Silverman
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TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Density Estimation for Statistics and Data Analysis
TL;DR: Density estimation, as discussed in this book, is the construction of an estimate of the density function from the observed data from an unknown probability density function.
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Probability and Measure
Patrick Billingsley
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TL;DR: In this paper, the convergence of distributions is considered in the context of conditional probability, i.e., random variables and expected values, and the probability of a given distribution converging to a certain value.
A Random Approach to Time-Optimal Control
TL;DR: This paper concerns the time-optimal control for objects described by a random differential inclusion with discontinous right-hand side, representing the second law of Newtonian mechanics and taking into account a complex model of resistance to motion.
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