A neurofuzzy adaptive kalman filter

P. J. Escamilla-Ambrosio

doi:10.1109/IS.2006.348485

A neurofuzzy adaptive kalman filter

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is integrated into a neurofuzzy network structure to perform system identification and state estimation of unknown nonlinear systems. This approach, referred to as neurofuzzy adaptive Kalman filter, uses the error signal in the identification process as the measurement noise signal for the FL-AKF in order to estimate the modelling error at the same time in which system identification is performed by the neurofuzzy network. This has a stabilisation effect during the training process when noise is present in the training data. A simulated example is presented to validate the effectiveness of the proposed approach.

Original language	English
Title of host publication	2006 3rd International IEEE Conference Intelligent Systems, IS'06
Pages	588-593
Number of pages	6
DOIs	https://doi.org/10.1109/IS.2006.348485
State	Published - 2006
Externally published	Yes
Event	2006 3rd International IEEE Conference Intelligent Systems, IS'06 - London, United Kingdom Duration: 4 Sep 2006 → 6 Sep 2006

Conference

Conference	2006 3rd International IEEE Conference Intelligent Systems, IS'06
Country/Territory	United Kingdom
City	London
Period	4/09/06 → 6/09/06

Keywords

Adaptive systems
Kalman filter
Modelling and system identification
Neurofuzzy systems
Nonlinear systems
State estimation

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10.1109/IS.2006.348485

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title = "A neurofuzzy adaptive kalman filter",

abstract = "In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is integrated into a neurofuzzy network structure to perform system identification and state estimation of unknown nonlinear systems. This approach, referred to as neurofuzzy adaptive Kalman filter, uses the error signal in the identification process as the measurement noise signal for the FL-AKF in order to estimate the modelling error at the same time in which system identification is performed by the neurofuzzy network. This has a stabilisation effect during the training process when noise is present in the training data. A simulated example is presented to validate the effectiveness of the proposed approach.",

keywords = "Adaptive systems, Kalman filter, Modelling and system identification, Neurofuzzy systems, Nonlinear systems, State estimation",

author = "Escamilla-Ambrosio, {P. J.}",

year = "2006",

doi = "10.1109/IS.2006.348485",

language = "Ingl{\'e}s",

isbn = "1424401968",

pages = "588--593",

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note = "2006 3rd International IEEE Conference Intelligent Systems, IS'06 ; Conference date: 04-09-2006 Through 06-09-2006",

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AU - Escamilla-Ambrosio, P. J.

PY - 2006

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N2 - In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is integrated into a neurofuzzy network structure to perform system identification and state estimation of unknown nonlinear systems. This approach, referred to as neurofuzzy adaptive Kalman filter, uses the error signal in the identification process as the measurement noise signal for the FL-AKF in order to estimate the modelling error at the same time in which system identification is performed by the neurofuzzy network. This has a stabilisation effect during the training process when noise is present in the training data. A simulated example is presented to validate the effectiveness of the proposed approach.

AB - In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is integrated into a neurofuzzy network structure to perform system identification and state estimation of unknown nonlinear systems. This approach, referred to as neurofuzzy adaptive Kalman filter, uses the error signal in the identification process as the measurement noise signal for the FL-AKF in order to estimate the modelling error at the same time in which system identification is performed by the neurofuzzy network. This has a stabilisation effect during the training process when noise is present in the training data. A simulated example is presented to validate the effectiveness of the proposed approach.

KW - Adaptive systems

KW - Kalman filter

KW - Modelling and system identification

KW - Neurofuzzy systems

KW - Nonlinear systems

KW - State estimation

UR - http://www.scopus.com/inward/record.url?scp=46249095729&partnerID=8YFLogxK

U2 - 10.1109/IS.2006.348485

DO - 10.1109/IS.2006.348485

M3 - Contribución a la conferencia

AN - SCOPUS:46249095729

SN - 1424401968

SN - 9781424401963

SP - 588

EP - 593

BT - 2006 3rd International IEEE Conference Intelligent Systems, IS'06

T2 - 2006 3rd International IEEE Conference Intelligent Systems, IS'06

Y2 - 4 September 2006 through 6 September 2006

ER -

A neurofuzzy adaptive kalman filter

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Keywords

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