Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets
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 mo...
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| Format: | Article |
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MDPI AG
2017-08-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/10/3/91 |
| _version_ | 1828466570483990528 |
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| author | Chengyuan Chen Qiang Shen |
| author_facet | Chengyuan Chen Qiang Shen |
| author_sort | Chengyuan Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T03:52:39Z |
| format | Article |
| id | doaj.art-eb00f0d402754b6bb8a809955a66c8e2 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-11T03:52:39Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-eb00f0d402754b6bb8a809955a66c8e22022-12-22T01:21:52ZengMDPI AGAlgorithms1999-48932017-08-011039110.3390/a10030091a10030091Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy SetsChengyuan Chen0Qiang Shen1School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaDepartment of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UKIn 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.https://www.mdpi.com/1999-4893/10/3/91fuzzy rule interpolationinterval type-2 fuzzy setstransformation-based interpolation |
| spellingShingle | Chengyuan Chen Qiang Shen Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets Algorithms fuzzy rule interpolation interval type-2 fuzzy sets transformation-based interpolation |
| title | Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets |
| title_full | Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets |
| title_fullStr | Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets |
| title_full_unstemmed | Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets |
| title_short | Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets |
| title_sort | transformation based fuzzy rule interpolation using interval type 2 fuzzy sets |
| topic | fuzzy rule interpolation interval type-2 fuzzy sets transformation-based interpolation |
| url | https://www.mdpi.com/1999-4893/10/3/91 |
| work_keys_str_mv | AT chengyuanchen transformationbasedfuzzyruleinterpolationusingintervaltype2fuzzysets AT qiangshen transformationbasedfuzzyruleinterpolationusingintervaltype2fuzzysets |