Design of a robust interval-valued type-2 fuzzy c-regression model for a nonlinear system with noise and outliers

Design of a robust interval-valued type-2 fuzzy c-regression model for a nonlinear system with... This paper presents the development of a novel interval-valued type-2 robust fuzzy c-regression model (IVT2RFCRM) clustering algorithm for identification of nonlinear systems taking into account the presence of noise and outliers in the associated dataset. On the one hand, the proposed method allows for the handling of the uncertainties of the FCRM due to its fixed fuzzier parameter m. In the other hand, the dataset is subject to various sources of uncertainty such as measurement uncertainty, fuzziness of information and environmental noise. As a result, obtaining a high-quality approximation of real processes is often a difficult task. In this paper, the structure of the proposed clustering algorithm is given and its parameter update rule is derived. First, the modified objective functions use a kernel measure of error to deal with the noisy data. Then, a credibility function is integrated into the clustering process in order to reduce the effect of outliers. Finally, the effectiveness of the proposed algorithm is evaluated by comparing the obtained results with others reported in the literature and also through the simulation results of a real liquid level process. Keywords Type-2 fuzzy systems · Identification · Fuzzy c-regression model · Kernel approach · Noise clustering http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

Design of a robust interval-valued type-2 fuzzy c-regression model for a nonlinear system with noise and outliers

Loading next page...
 
/lp/springer_journal/design-of-a-robust-interval-valued-type-2-fuzzy-c-regression-model-for-vILKnGPe08
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
D.O.I.
10.1007/s00500-018-3265-z
Publisher site
See Article on Publisher Site

Abstract

This paper presents the development of a novel interval-valued type-2 robust fuzzy c-regression model (IVT2RFCRM) clustering algorithm for identification of nonlinear systems taking into account the presence of noise and outliers in the associated dataset. On the one hand, the proposed method allows for the handling of the uncertainties of the FCRM due to its fixed fuzzier parameter m. In the other hand, the dataset is subject to various sources of uncertainty such as measurement uncertainty, fuzziness of information and environmental noise. As a result, obtaining a high-quality approximation of real processes is often a difficult task. In this paper, the structure of the proposed clustering algorithm is given and its parameter update rule is derived. First, the modified objective functions use a kernel measure of error to deal with the noisy data. Then, a credibility function is integrated into the clustering process in order to reduce the effect of outliers. Finally, the effectiveness of the proposed algorithm is evaluated by comparing the obtained results with others reported in the literature and also through the simulation results of a real liquid level process. Keywords Type-2 fuzzy systems · Identification · Fuzzy c-regression model · Kernel approach · Noise clustering

Journal

Soft ComputingSpringer Journals

Published: Jun 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off