{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:14:10Z","timestamp":1760242450258,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,8,15]],"date-time":"2017-08-15T00:00:00Z","timestamp":1502755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Research Program of Chongqing Municipal Education Commission, China","award":["KJ1601316"],"award-info":[{"award-number":["KJ1601316"]}]},{"name":"Research Foundation of Chongqing University of Science and Technology, China","award":["CK2016B04"],"award-info":[{"award-number":["CK2016B04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a10030091","type":"journal-article","created":{"date-parts":[[2017,8,15]],"date-time":"2017-08-15T14:35:00Z","timestamp":1502807700000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets"],"prefix":"10.3390","volume":"10","author":[{"given":"Chengyuan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China"}]},{"given":"Qiang","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/TSMC.1973.5408575","article-title":"Outline of a new approach to the analysis of complex systems and decision processes","volume":"3","author":"Zadeh","year":"1973","journal-title":"IEEE Trans. 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