Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo)
Multivariate identifier filters (multiple inputs and multiple outputs - MIMO) are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy l...
- Autores:
-
García Infante, Juan Carlos
Medel Juárez, José de J.
Sánchez García, Juan Carlos
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2011
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/33506
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/33506
http://bdigital.unal.edu.co/23586/
http://bdigital.unal.edu.co/23586/2/
http://bdigital.unal.edu.co/23586/3/
- Palabra clave:
- filtro digital
control difuso
red neuronal
MIMO
adaptivo.
digital filter
fuzzy control
neural network
MIMO
adaptive digital system.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo) |
| title |
Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo) |
| spellingShingle |
Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo) filtro digital control difuso red neuronal MIMO adaptivo. digital filter fuzzy control neural network MIMO adaptive digital system. |
| title_short |
Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo) |
| title_full |
Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo) |
| title_fullStr |
Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo) |
| title_full_unstemmed |
Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo) |
| title_sort |
Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo) |
| dc.creator.fl_str_mv |
García Infante, Juan Carlos Medel Juárez, José de J. Sánchez García, Juan Carlos |
| dc.contributor.author.spa.fl_str_mv |
García Infante, Juan Carlos Medel Juárez, José de J. Sánchez García, Juan Carlos |
| dc.subject.proposal.spa.fl_str_mv |
filtro digital control difuso red neuronal MIMO adaptivo. digital filter fuzzy control neural network MIMO adaptive digital system. |
| topic |
filtro digital control difuso red neuronal MIMO adaptivo. digital filter fuzzy control neural network MIMO adaptive digital system. |
| description |
Multivariate identifier filters (multiple inputs and multiple outputs - MIMO) are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy logic inference mechanisms which interpret and select the best matrix parameter from a knowledge base. Such selection mechanisms with neural networks can provide a response from the best operational level for each change in state (Shannon, 1948). This paper considers the MIMO digital filter model using neuro fuzzy digital filtering to find an adaptive parameter matrix which is integrated into the Kalman filter by the transition matrix. The filter uses the neural network as back-propagation into the fuzzy mechanism to do this, interpreting its variables and its respective levels and selecting the best values for automatically adjusting transition matrix values. The Matlab simulation describes the neural fuzzy digital filter giving an approximation of exponential convergence seen in functional error. |
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2011 |
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2011 |
| dc.date.accessioned.spa.fl_str_mv |
2019-06-27T22:58:25Z |
| dc.date.available.spa.fl_str_mv |
2019-06-27T22:58:25Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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http://purl.org/coar/resource_type/c_6501 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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http://purl.org/coar/resource_type/c_6501 |
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https://repositorio.unal.edu.co/handle/unal/33506 |
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http://bdigital.unal.edu.co/23586/ http://bdigital.unal.edu.co/23586/2/ http://bdigital.unal.edu.co/23586/3/ |
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https://repositorio.unal.edu.co/handle/unal/33506 http://bdigital.unal.edu.co/23586/ http://bdigital.unal.edu.co/23586/2/ http://bdigital.unal.edu.co/23586/3/ |
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spa |
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spa |
| dc.relation.spa.fl_str_mv |
http://revistas.unal.edu.co/index.php/ingeinv/article/view/20569 |
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Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e Investigación Ingeniería e Investigación |
| dc.relation.ispartofseries.none.fl_str_mv |
Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 184-192 Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 184-192 2248-8723 0120-5609 |
| dc.relation.references.spa.fl_str_mv |
García Infante, Juan Carlos and Medel Juárez, José de J. and Sánchez García, Juan Carlos (2011) Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo). Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 184-192 Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 184-192 2248-8723 0120-5609 . |
| dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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Universidad Nacional de Colombia - Facultad de Ingeniería |
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Universidad Nacional de Colombia |
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2García Infante, Juan Carlos612dad56-342b-4807-9851-8da042368cd7300Medel Juárez, José de J.9282f151-20ac-4f9d-b853-4f033692e5ef300Sánchez García, Juan Carlosc425fe26-f090-4271-baed-b558465a225d3002019-06-27T22:58:25Z2019-06-27T22:58:25Z2011https://repositorio.unal.edu.co/handle/unal/33506http://bdigital.unal.edu.co/23586/http://bdigital.unal.edu.co/23586/2/http://bdigital.unal.edu.co/23586/3/Multivariate identifier filters (multiple inputs and multiple outputs - MIMO) are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy logic inference mechanisms which interpret and select the best matrix parameter from a knowledge base. Such selection mechanisms with neural networks can provide a response from the best operational level for each change in state (Shannon, 1948). This paper considers the MIMO digital filter model using neuro fuzzy digital filtering to find an adaptive parameter matrix which is integrated into the Kalman filter by the transition matrix. The filter uses the neural network as back-propagation into the fuzzy mechanism to do this, interpreting its variables and its respective levels and selecting the best values for automatically adjusting transition matrix values. The Matlab simulation describes the neural fuzzy digital filter giving an approximation of exponential convergence seen in functional error.Los filtros identificadores multivariables (MIMO) son sistemas digitales adaptivos que cuentan con retroalimentación para que, de acuerdo a una función objetivo, ajusten su matriz de parámetros con la que se aproximan a la di-námica observable del sistema de referencia. Una forma de que un identificador cumpla con esas condiciones, es la de la lógica difusa por medio de sus mecanismos de in-ferencia que interpretan y seleccionan en una base de co-nocimiento la mejor matriz de parámetros. Estos mecanismos de selección mediante las redes neuronales permiten encontrar la respuesta con el mejor nivel de operación para cada cambio de estado (Shannon, 1948). En este artículo se considera en el modelo MIMO del filtrado digital, el proceso neuronal difuso para la estimación matricial de parámetros adaptiva, que se integra en el filtro de Kalman a través de la matriz de transición. Para ello se utilizó la red neuronal del tipo retropropagación en el mecanismo difuso, interpretando sus variables y sus respectivos niveles, seleccionando los mejores valores para ajustar automáticamente los valores de la matriz de transición. La simulación en Matlab presenta al filtrado digital neuronal difuso dando el seguimiento, observándose un funcional de error decreciente exponencialmente.application/pdfspaUniversidad Nacional de Colombia - Facultad de Ingenieríahttp://revistas.unal.edu.co/index.php/ingeinv/article/view/20569Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e InvestigaciónIngeniería e InvestigaciónIngeniería e Investigación; Vol. 31, núm. 1 (2011); 184-192 Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 184-192 2248-8723 0120-5609García Infante, Juan Carlos and Medel Juárez, José de J. and Sánchez García, Juan Carlos (2011) Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo). Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 184-192 Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 184-192 2248-8723 0120-5609 .Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (mimo)Artículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTfiltro digitalcontrol difusored neuronalMIMOadaptivo.digital filterfuzzy controlneural networkMIMOadaptive digital system.ORIGINAL20569-69492-1-PB.pdfapplication/pdf776891https://repositorio.unal.edu.co/bitstream/unal/33506/1/20569-69492-1-PB.pdfef195fb43943cefd2b2b45e6b41914d1MD51THUMBNAIL20569-69492-1-PB.pdf.jpg20569-69492-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg9100https://repositorio.unal.edu.co/bitstream/unal/33506/2/20569-69492-1-PB.pdf.jpg33ec431fc9cf82501622e4bca13dbfb3MD52unal/33506oai:repositorio.unal.edu.co:unal/335062022-12-26 23:04:58.382Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |