Next Article in Journal
Automatic Modulation Recognition Using Compressive Cyclic Features
Next Article in Special Issue
Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems
Previous Article in Journal
Local Community Detection Based on Small Cliques
Previous Article in Special Issue
A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot
Open AccessArticle

Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets

School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
Author to whom correspondence should be addressed.
Algorithms 2017, 10(3), 91;
Received: 18 June 2017 / Revised: 1 August 2017 / Accepted: 10 August 2017 / Published: 15 August 2017
(This article belongs to the Special Issue Extensions to Type-1 Fuzzy Logic: Theory, Algorithms and Applications)
PDF [889 KB, uploaded 15 August 2017]


In support of reasoning with sparse rule bases, fuzzy rule interpolation (FRI) offers a helpful inference mechanism for deriving an approximate conclusion when a given observation has no overlap with any rule in the existing rule base. One of the recent and popular FRI approaches is the scale and move transformation-based rule interpolation, known as T-FRI in the literature. It supports both interpolation and extrapolation with multiple multi-antecedent rules. However, the difficult problem of defining the precise-valued membership functions required in the representation of fuzzy rules, or of the observations, restricts its applications. Fortunately, this problem can be alleviated through the use of type-2 fuzzy sets, owing to the fact that the membership functions of such fuzzy sets are themselves fuzzy, providing a more flexible means of modelling. This paper therefore, extends the existing T-FRI approach using interval type-2 fuzzy sets, which covers the original T-FRI as its specific instance. The effectiveness of this extension is demonstrated by experimental investigations and, also, by a practical application in comparison to the state-of-the-art alternative approach developed using rough-fuzzy sets. View Full-Text
Keywords: fuzzy rule interpolation; interval type-2 fuzzy sets; transformation-based interpolation fuzzy rule interpolation; interval type-2 fuzzy sets; transformation-based interpolation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Chen, C.; Shen, Q. Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets. Algorithms 2017, 10, 91.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top