A Neuro-Fuzzy based framework for online nonlinear system identification

Paulo Gil; Tiago Oliveira; Luís Palma

doi:10.1145/3057039.3057068

A Neuro-Fuzzy based framework for online nonlinear system identification

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

Abstract

In this paper a new general recurrent state-space Neuro-Fuzzy model structure based on the combination of a modified Jordan network and an Adaptive Neuro-Fuzzy Inference System is proposed. The Neural-Fuzzy System's online training is carried out based on a Constrained Unscented Kalman Filter, where weights, membership functions and consequents are recursively updated. Results from a benchmark MIMO system demonstrate the applicability and effectiveness of the proposed framework.

Original language	English
Title of host publication	ACM International Conference Proceeding Series
Pages	248-253
Number of pages	6
DOIs	https://doi.org/10.1145/3057039.3057068
Publication status	Published - 18 Feb 2017
Event	9th International Conference on Computer and Automation Engineering, ICCAE 2017 - Sydney, Australia Duration: 18 Feb 2017 → 21 Feb 2017

Conference

Conference	9th International Conference on Computer and Automation Engineering, ICCAE 2017
Country/Territory	Australia
City	Sydney
Period	18/02/17 → 21/02/17

Keywords

Kalman filter
Neuro-Fuzzy systems
Nonlinear system identification
Time-varying dynamics
Unscented transform

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10.1145/3057039.3057068Licence: Unspecified

Cite this

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title = "A Neuro-Fuzzy based framework for online nonlinear system identification",

abstract = "In this paper a new general recurrent state-space Neuro-Fuzzy model structure based on the combination of a modified Jordan network and an Adaptive Neuro-Fuzzy Inference System is proposed. The Neural-Fuzzy System's online training is carried out based on a Constrained Unscented Kalman Filter, where weights, membership functions and consequents are recursively updated. Results from a benchmark MIMO system demonstrate the applicability and effectiveness of the proposed framework.",

keywords = "Kalman filter, Neuro-Fuzzy systems, Nonlinear system identification, Time-varying dynamics, Unscented transform",

author = "Paulo Gil and Tiago Oliveira and Lu{\'i}s Palma",

year = "2017",

month = feb,

day = "18",

doi = "10.1145/3057039.3057068",

language = "English",

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T1 - A Neuro-Fuzzy based framework for online nonlinear system identification

AU - Gil, Paulo

AU - Oliveira, Tiago

AU - Palma, Luís

PY - 2017/2/18

Y1 - 2017/2/18

N2 - In this paper a new general recurrent state-space Neuro-Fuzzy model structure based on the combination of a modified Jordan network and an Adaptive Neuro-Fuzzy Inference System is proposed. The Neural-Fuzzy System's online training is carried out based on a Constrained Unscented Kalman Filter, where weights, membership functions and consequents are recursively updated. Results from a benchmark MIMO system demonstrate the applicability and effectiveness of the proposed framework.

AB - In this paper a new general recurrent state-space Neuro-Fuzzy model structure based on the combination of a modified Jordan network and an Adaptive Neuro-Fuzzy Inference System is proposed. The Neural-Fuzzy System's online training is carried out based on a Constrained Unscented Kalman Filter, where weights, membership functions and consequents are recursively updated. Results from a benchmark MIMO system demonstrate the applicability and effectiveness of the proposed framework.

KW - Kalman filter

KW - Neuro-Fuzzy systems

KW - Nonlinear system identification

KW - Time-varying dynamics

KW - Unscented transform

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U2 - 10.1145/3057039.3057068

DO - 10.1145/3057039.3057068

M3 - Conference contribution

AN - SCOPUS:85020906978

SN - 978-1-4503-4809-6

SP - 248

EP - 253

BT - ACM International Conference Proceeding Series

T2 - 9th International Conference on Computer and Automation Engineering, ICCAE 2017

Y2 - 18 February 2017 through 21 February 2017

ER -

A Neuro-Fuzzy based framework for online nonlinear system identification

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