Filter Estimator Based on the Probability Distribution
In this paper, we propose a model of a parameter estimator filter for Black Box-type Stochastic Systems (BBSS), that is, only its inputs and outputs are known; considering its intrinsic properties observed in the second probability moment, in which the probability has the description of a specific d...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10854470/ |
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| author | Romeo Urbieta Parrazalez Rosaura Palma Orozco Maria Teresa Zagaceta Alvarez Jose Luis Fernandez Munoz Karen Alicia Aguilar Cruz |
| author_facet | Romeo Urbieta Parrazalez Rosaura Palma Orozco Maria Teresa Zagaceta Alvarez Jose Luis Fernandez Munoz Karen Alicia Aguilar Cruz |
| author_sort | Romeo Urbieta Parrazalez |
| collection | DOAJ |
| description | In this paper, we propose a model of a parameter estimator filter for Black Box-type Stochastic Systems (BBSS), that is, only its inputs and outputs are known; considering its intrinsic properties observed in the second probability moment, in which the probability has the description of a specific distribution function relative to the random variables and the law of large numbers, allowing the stochastic system to be described with a Gaussian Distribution Function (GDF) and its linear transformation core in the Taylor series. Commonly, all estimates are assumed in the Second Moment of Probability (SMP) with equiprobable conditions, but in this case, a specific description of the Probability Distribution Function (PDF) is considered a priori, which is a contribution to the theory of digital filtering in estimation for signal reconstruction. The simulation of the parameter estimation is carried out for a given reference signal, maintaining the stability of the system in discrete time, and a comparison is made between the estimator based on the SMP and the PDF to observe the level of convergence, where the PDF approach has a better performance respect to the SMP one. |
| format | Article |
| id | doaj-art-358360325e734b83818b9e573c6fb997 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-358360325e734b83818b9e573c6fb9972025-01-31T23:05:14ZengIEEEIEEE Access2169-35362025-01-0113207132071910.1109/ACCESS.2025.353418010854470Filter Estimator Based on the Probability DistributionRomeo Urbieta Parrazalez0Rosaura Palma Orozco1https://orcid.org/0000-0003-0208-1644Maria Teresa Zagaceta Alvarez2https://orcid.org/0000-0003-1084-8569Jose Luis Fernandez Munoz3https://orcid.org/0000-0002-2039-3222Karen Alicia Aguilar Cruz4Laboratorio de Microtecnología y Sistemas Embebidos, CIC, Instituto Politécnico Nacional, Mexico City, MexicoLaboratorio Transdisciplinario de Investigación en Sistemas Evolutivos, ESCOM, Instituto Politécnico Nacional, Mexico City, MexicoCiencia de los Materiales, ESIME Azcapotzalco, Instituto Politécnico Nacional, Mexico City, MexicoPosgrado en Tecnología Avanzada, CICATA Legaria, Instituto Politécnico Nacional, Mexico City, MexicoAcademia de Computación, ESIME Zacatenco, Instituto Politécnico Nacional, Mexico City, MexicoIn this paper, we propose a model of a parameter estimator filter for Black Box-type Stochastic Systems (BBSS), that is, only its inputs and outputs are known; considering its intrinsic properties observed in the second probability moment, in which the probability has the description of a specific distribution function relative to the random variables and the law of large numbers, allowing the stochastic system to be described with a Gaussian Distribution Function (GDF) and its linear transformation core in the Taylor series. Commonly, all estimates are assumed in the Second Moment of Probability (SMP) with equiprobable conditions, but in this case, a specific description of the Probability Distribution Function (PDF) is considered a priori, which is a contribution to the theory of digital filtering in estimation for signal reconstruction. The simulation of the parameter estimation is carried out for a given reference signal, maintaining the stability of the system in discrete time, and a comparison is made between the estimator based on the SMP and the PDF to observe the level of convergence, where the PDF approach has a better performance respect to the SMP one.https://ieeexplore.ieee.org/document/10854470/Filter estimatordistribution functionstochastic systemsblack box |
| spellingShingle | Romeo Urbieta Parrazalez Rosaura Palma Orozco Maria Teresa Zagaceta Alvarez Jose Luis Fernandez Munoz Karen Alicia Aguilar Cruz Filter Estimator Based on the Probability Distribution IEEE Access Filter estimator distribution function stochastic systems black box |
| title | Filter Estimator Based on the Probability Distribution |
| title_full | Filter Estimator Based on the Probability Distribution |
| title_fullStr | Filter Estimator Based on the Probability Distribution |
| title_full_unstemmed | Filter Estimator Based on the Probability Distribution |
| title_short | Filter Estimator Based on the Probability Distribution |
| title_sort | filter estimator based on the probability distribution |
| topic | Filter estimator distribution function stochastic systems black box |
| url | https://ieeexplore.ieee.org/document/10854470/ |
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