{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:04:22Z","timestamp":1777705462007,"version":"3.51.4"},"reference-count":49,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,3,9]]},"abstract":"<jats:p>Fuzzy clustering has been widely applied in T-S fuzzy model identification for nonlinear systems, however, tradition type-1 fuzzy clustering algorithms can\u2019t deal with uncertainties in real world, an improved interval type-2 fuzzy c-regression model (IT2-FCRM) clustering is proposed for T-S fuzzy model identification in this paper. The improved IT2-FCRM adapts a new objective function, which makes the boundary of clustering more clearly and reduces the influence of outliers or noisy data on clustering results. The premise parameters of T-S fuzzy model are upper and lower hyperplanes obtained by improved IT2-FCRM, and the upper and lower hyperplanes are used to build hyper-plane-shaped type-2 Gaussian membership function. Compared with the hyper-sphere-shaped membership function of tradition IT2-FCRM, the hyper-plane-shaped membership function is more coincided with point to plane sample distance described by FCRM clustering. The simulation results of several benchmark problems and a real bed temperature in circulating fluidized bed plant show that the identification algorithm has higher accuracy.<\/jats:p>","DOI":"10.3233\/jifs-221434","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T10:07:49Z","timestamp":1670580469000},"page":"4495-4507","source":"Crossref","is-referenced-by-count":2,"title":["T-S fuzzy model identification based on an improved interval type-2 fuzzy c-regression model"],"prefix":"10.1177","volume":"44","author":[{"given":"Jianzhong","family":"Shi","sequence":"first","affiliation":[{"name":"School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-221434_ref1","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","article-title":"Fuzzy identification of systems and its applications to 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