NDLI: T-S fuzzy model identification and the fuzzy model based controller design

Content Provider	IEEE Xplore Digital Library
Author	Chung-Chun Kung Jui-Yiao Su
Copyright Year	2007
Description	Author affiliation: Tatung Univ., Taipei (Chung-Chun Kung; Jui-Yiao Su)
Abstract	This paper presents an algorithm to identify T-S fuzzy models and design fuzzy model based controllers (FMBC) for a class of nonlinear plant. First, the algorithm using fuzzy c-regression models (FCRM) clustering to find the functional relationships in the product space of the input-output data. A new cluster validity criterion is proposed to calculate overall compactness and separateness of the FCRM partition and then determine the appropriate number of regression models. Besides, the fine-tuning of the antecedent fuzzy set and the consequent parameters are considered. Thus, an efficient T-S fuzzy model with compact if-then rules can be generated systematically. Finally, an FMBC is proposed to make the nonlinear plant track the reference trajectory signal. A simulation example is provided to demonstrate the accuracy and effectiveness of the proposed algorithm.
Starting Page	859
Ending Page	864
File Size	232524
Page Count	6
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4413895
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy control Clustering algorithms Partitioning algorithms Algorithm design and analysis Fuzzy systems Prototypes Fuzzy sets Trajectory Entropy Vectors
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

T–S Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm

T-S fuzzy model identification of MIMO nonlinear systems based on data-driven

A new cluster validity criterion for the cross iterative fuzzy clustering algorithm

Parameter selection for suppressed fuzzy c-means clustering algorithm based on fuzzy partition entropy

Fuzzy identification based on improved T-S fuzzy model and its application in power plants

MECA: maximum entropy clustering algorithm

Fuzzy C-Means Clustering Algorithm Based on Incomplete Data

Maximum-entropy-based multiple kernel fuzzy c-means clustering algorithm

A new algorithm for T-S fuzzy modeling based on hierarchical fuzzy-clustering

T-S fuzzy model identification and the fuzzy model based controller design

Similar Documents

T–S Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm

T-S fuzzy model identification of MIMO nonlinear systems based on data-driven

A new cluster validity criterion for the cross iterative fuzzy clustering algorithm

Parameter selection for suppressed fuzzy c-means clustering algorithm based on fuzzy partition entropy

Fuzzy identification based on improved T-S fuzzy model and its application in power plants

MECA: maximum entropy clustering algorithm

Fuzzy C-Means Clustering Algorithm Based on Incomplete Data

Maximum-entropy-based multiple kernel fuzzy c-means clustering algorithm

A new algorithm for T-S fuzzy modeling based on hierarchical fuzzy-clustering

T-S fuzzy model identification and the fuzzy model based controller design