{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:48:57Z","timestamp":1760237337635,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,5]],"date-time":"2020-04-05T00:00:00Z","timestamp":1586044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The aim of this study was to develop a novel intuitionistic Type-2 fuzzy inference system (IT-2 FIS) which adopts a parameterized Yager-generating function and particle swarm optimization (PSO). In IT-2 FIS, the intuitionistic Type-2 is set as a fuzzy symmetrical triangular number in which the hesitation degree adopts the Yager-generating function, and the parameters of the proposed IT-2 FIS adopting the PSO are tuned. The intuitionistic and Type-2 fuzzy sets have been proven to be the most effective for handling more uncertainty. Therefore, this study proposes an intuitionistic Type-2 set with a Yager-generating function to enhance the conventional fuzzy inference system. Moreover, PSO can improve the fuzzy inference system by searching for the optimal parameters of IT-2 FIS. In this study, linguistic variables were represented by triangular fuzzy numbers (TFS). Two numerical examples were examined: capacity-planning and medical diagnosis problems. An approaching capacity-loadings example was used to verify that the proposed IT-2 FIS could effectively estimate the results of the capacity loadings. In the medical diagnosis problem, IT-2 FIS could obtain a higher correct rate by revealing experts\u2019 knowledge. In both examples, the proposed IT-2 FIS provided more objective estimated values than traditional fuzzy inference systems (FIS) and Type-2 FIS.<\/jats:p>","DOI":"10.3390\/sym12040562","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T03:40:19Z","timestamp":1586403619000},"page":"562","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization"],"prefix":"10.3390","volume":"12","author":[{"given":"Chun-Min","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8649-8959","authenticated-orcid":false,"given":"Kuo-Ping","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan"}]},{"given":"Gia-Shie","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 33306, Taiwan"}]},{"given":"Chia-Hao","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 33306, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0165-0114(78)90029-5","article-title":"Fuzzy sets as a basis for theory of possibility","volume":"1","author":"Zadeh","year":"1978","journal-title":"Fuzzy Sets Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.envint.2006.03.009","article-title":"Assessing water quality in rivers with fuzzy inference systems: A case study","volume":"32","author":"Ocampo","year":"2006","journal-title":"Environ. 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