Subspace identification of dynamical neurofuzzy system using LOLIMOT

Mahmood Mola; Mojtaba Ahmadieh Khanesar; Mohammad Teshnehlab

doi:10.1109/ICSMC.2010.5641736

Subspace identification of dynamical neurofuzzy system using LOLIMOT

Mahmood Mola^*, Mojtaba Ahmadieh Khanesar, Mohammad Teshnehlab

^*Corresponding author for this work

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

Abstract

In this paper a novel method for identification of dynamical neurofuzzy system is proposed. The proposed method benefits from both LOLIMOT as the premise part optimizer of the system and the subspace identification method of N4SID to optimize the state space parameters of the conclusion part. The resulting neurofuzzy system is a nonlinear dynamical system which is modeled by some locally linear state space models. using this model it is then possible to use different parallel distributed control techniques such as linear matrix inequality to control the identified system. The proposed approach is tested on a flexible robot arm and satisfactory results are generated.

Original language	English
Title of host publication	2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
Number of pages	7
Publication date	2010
Pages	366-372
Article number	5641736
ISBN (Print)	9781424465880
DOIs	https://doi.org/10.1109/ICSMC.2010.5641736
Publication status	Published - 2010
Externally published	Yes
Event	2010 IEEE International Conference on Systems, Man and Cybernetics - Istanbul, Turkey Duration: 10 Oct 2010 → 13 Oct 2010 https://ieeexplore.ieee.org/xpl/conhome/5629466/proceeding

Conference

Conference	2010 IEEE International Conference on Systems, Man and Cybernetics
Country/Territory	Turkey
City	Istanbul
Period	10/10/2010 → 13/10/2010
Internet address	https://ieeexplore.ieee.org/xpl/conhome/5629466/proceeding

Keywords

LOLIMOT
N4SID
Neurofuzzy
Nonlinear identification
Subspace identification

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10.1109/ICSMC.2010.5641736

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title = "Subspace identification of dynamical neurofuzzy system using LOLIMOT",

abstract = "In this paper a novel method for identification of dynamical neurofuzzy system is proposed. The proposed method benefits from both LOLIMOT as the premise part optimizer of the system and the subspace identification method of N4SID to optimize the state space parameters of the conclusion part. The resulting neurofuzzy system is a nonlinear dynamical system which is modeled by some locally linear state space models. using this model it is then possible to use different parallel distributed control techniques such as linear matrix inequality to control the identified system. The proposed approach is tested on a flexible robot arm and satisfactory results are generated.",

keywords = "LOLIMOT, N4SID, Neurofuzzy, Nonlinear identification, Subspace identification",

author = "Mahmood Mola and Khanesar, {Mojtaba Ahmadieh} and Mohammad Teshnehlab",

year = "2010",

doi = "10.1109/ICSMC.2010.5641736",

language = "English",

isbn = "9781424465880",

pages = "366--372",

booktitle = "2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010",

note = "2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 ; Conference date: 10-10-2010 Through 13-10-2010",

url = "https://ieeexplore.ieee.org/xpl/conhome/5629466/proceeding",

}

TY - GEN

T1 - Subspace identification of dynamical neurofuzzy system using LOLIMOT

AU - Mola, Mahmood

AU - Khanesar, Mojtaba Ahmadieh

AU - Teshnehlab, Mohammad

PY - 2010

Y1 - 2010

N2 - In this paper a novel method for identification of dynamical neurofuzzy system is proposed. The proposed method benefits from both LOLIMOT as the premise part optimizer of the system and the subspace identification method of N4SID to optimize the state space parameters of the conclusion part. The resulting neurofuzzy system is a nonlinear dynamical system which is modeled by some locally linear state space models. using this model it is then possible to use different parallel distributed control techniques such as linear matrix inequality to control the identified system. The proposed approach is tested on a flexible robot arm and satisfactory results are generated.

AB - In this paper a novel method for identification of dynamical neurofuzzy system is proposed. The proposed method benefits from both LOLIMOT as the premise part optimizer of the system and the subspace identification method of N4SID to optimize the state space parameters of the conclusion part. The resulting neurofuzzy system is a nonlinear dynamical system which is modeled by some locally linear state space models. using this model it is then possible to use different parallel distributed control techniques such as linear matrix inequality to control the identified system. The proposed approach is tested on a flexible robot arm and satisfactory results are generated.

KW - LOLIMOT

KW - N4SID

KW - Neurofuzzy

KW - Nonlinear identification

KW - Subspace identification

UR - http://www.scopus.com/inward/record.url?scp=78751483396&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2010.5641736

DO - 10.1109/ICSMC.2010.5641736

M3 - Article in proceedings

AN - SCOPUS:78751483396

SN - 9781424465880

SP - 366

EP - 372

BT - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010

T2 - 2010 IEEE International Conference on Systems, Man and Cybernetics

Y2 - 10 October 2010 through 13 October 2010

ER -

Subspace identification of dynamical neurofuzzy system using LOLIMOT

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