Fuzzy horizon group shift FIR filtering for nonlinear systems with Takagi-Sugeno model

Jung Min Pak; Choon Ki Ahn; Chang Joo Lee; Peng Shi; Myo Taeg Lim; Moon Kyou Song

doi:10.1016/j.neucom.2015.10.029

Fuzzy horizon group shift FIR filtering for nonlinear systems with Takagi-Sugeno model

Jung Min Pak, Choon Ki Ahn, Chang Joo Lee, Peng Shi, Myo Taeg Lim, Moon Kyou Song

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

Abstract

In recent years, the Takagi-Sugeno (T-S) fuzzy model has been commonly used for the approximation of nonlinear systems. Using the T-S fuzzy model, nonlinear systems can be converted into linear time-varying systems, which can reduce approximation errors compared with the conventional Taylor approximation. In this paper, we propose a new nonlinear filter with a finite impulse response (FIR) structure based on the T-S fuzzy model. We firstly derive the fuzzy FIR filter and combine it with the horizon group shift (HGS) algorithm to manage the horizon size, which is an important design parameter of FIR filters. The resulting filter is called the fuzzy HGS FIR filter (FHFF). Due to the FIR structure, the FHFF has robustness against model parameter uncertainties. We demonstrate the performance of the FHFF in comparison with existing nonlinear filters, such as the fuzzy Kalman filter and the particle filter.

Original language	English
Pages (from-to)	1013-1020
Number of pages	8
Journal	Neurocomputing
Volume	174
DOIs	https://doi.org/10.1016/j.neucom.2015.10.029
Publication status	Published - 2016 Jan 22

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT and Future Planning under Grant NRF-2014R1A1A1006101, in part by 'Human Resources program in Energy Technology' of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20154030200610), and in part by Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2013R1A1A2008698).

Publisher Copyright:
© 2015 Elsevier B.V..

Keywords

Finite impulse response (FIR) filter
Horizon group shift (HGS)
Nonlinear systems
State estimation
Takagi-Sugeno (T-S) fuzzy model

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2015.10.029

Cite this

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title = "Fuzzy horizon group shift FIR filtering for nonlinear systems with Takagi-Sugeno model",

abstract = "In recent years, the Takagi-Sugeno (T-S) fuzzy model has been commonly used for the approximation of nonlinear systems. Using the T-S fuzzy model, nonlinear systems can be converted into linear time-varying systems, which can reduce approximation errors compared with the conventional Taylor approximation. In this paper, we propose a new nonlinear filter with a finite impulse response (FIR) structure based on the T-S fuzzy model. We firstly derive the fuzzy FIR filter and combine it with the horizon group shift (HGS) algorithm to manage the horizon size, which is an important design parameter of FIR filters. The resulting filter is called the fuzzy HGS FIR filter (FHFF). Due to the FIR structure, the FHFF has robustness against model parameter uncertainties. We demonstrate the performance of the FHFF in comparison with existing nonlinear filters, such as the fuzzy Kalman filter and the particle filter.",

keywords = "Finite impulse response (FIR) filter, Horizon group shift (HGS), Nonlinear systems, State estimation, Takagi-Sugeno (T-S) fuzzy model",

author = "Pak, {Jung Min} and Ahn, {Choon Ki} and Lee, {Chang Joo} and Peng Shi and Lim, {Myo Taeg} and Song, {Moon Kyou}",

note = "Funding Information: This work was supported in part by the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT and Future Planning under Grant NRF-2014R1A1A1006101, in part by 'Human Resources program in Energy Technology' of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20154030200610), and in part by Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2013R1A1A2008698). Publisher Copyright: {\textcopyright} 2015 Elsevier B.V..",

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AU - Shi, Peng

AU - Lim, Myo Taeg

AU - Song, Moon Kyou

N1 - Funding Information: This work was supported in part by the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT and Future Planning under Grant NRF-2014R1A1A1006101, in part by 'Human Resources program in Energy Technology' of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20154030200610), and in part by Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2013R1A1A2008698). Publisher Copyright: © 2015 Elsevier B.V..

PY - 2016/1/22

Y1 - 2016/1/22

N2 - In recent years, the Takagi-Sugeno (T-S) fuzzy model has been commonly used for the approximation of nonlinear systems. Using the T-S fuzzy model, nonlinear systems can be converted into linear time-varying systems, which can reduce approximation errors compared with the conventional Taylor approximation. In this paper, we propose a new nonlinear filter with a finite impulse response (FIR) structure based on the T-S fuzzy model. We firstly derive the fuzzy FIR filter and combine it with the horizon group shift (HGS) algorithm to manage the horizon size, which is an important design parameter of FIR filters. The resulting filter is called the fuzzy HGS FIR filter (FHFF). Due to the FIR structure, the FHFF has robustness against model parameter uncertainties. We demonstrate the performance of the FHFF in comparison with existing nonlinear filters, such as the fuzzy Kalman filter and the particle filter.

AB - In recent years, the Takagi-Sugeno (T-S) fuzzy model has been commonly used for the approximation of nonlinear systems. Using the T-S fuzzy model, nonlinear systems can be converted into linear time-varying systems, which can reduce approximation errors compared with the conventional Taylor approximation. In this paper, we propose a new nonlinear filter with a finite impulse response (FIR) structure based on the T-S fuzzy model. We firstly derive the fuzzy FIR filter and combine it with the horizon group shift (HGS) algorithm to manage the horizon size, which is an important design parameter of FIR filters. The resulting filter is called the fuzzy HGS FIR filter (FHFF). Due to the FIR structure, the FHFF has robustness against model parameter uncertainties. We demonstrate the performance of the FHFF in comparison with existing nonlinear filters, such as the fuzzy Kalman filter and the particle filter.

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Fuzzy horizon group shift FIR filtering for nonlinear systems with Takagi-Sugeno model

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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