{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:43:23Z","timestamp":1760150603202,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T00:00:00Z","timestamp":1702857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171287","62076165","JCYJ20220818100004008","2020KCXTD004","05202206"],"award-info":[{"award-number":["62171287","62076165","JCYJ20220818100004008","2020KCXTD004","05202206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science &amp; Technology Program of Shenzhen","award":["62171287","62076165","JCYJ20220818100004008","2020KCXTD004","05202206"],"award-info":[{"award-number":["62171287","62076165","JCYJ20220818100004008","2020KCXTD004","05202206"]}]},{"name":"Innovation Team Project of the Department of Education of Guangdong Province","award":["62171287","62076165","JCYJ20220818100004008","2020KCXTD004","05202206"],"award-info":[{"award-number":["62171287","62076165","JCYJ20220818100004008","2020KCXTD004","05202206"]}]},{"name":"Science and Technology on Information Systems Engineering Laboratory","award":["62171287","62076165","JCYJ20220818100004008","2020KCXTD004","05202206"],"award-info":[{"award-number":["62171287","62076165","JCYJ20220818100004008","2020KCXTD004","05202206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To realize aerial target recognition in a complex environment, we propose a multi-source Takagi\u2013Sugeno (T-S) intuitionistic fuzzy rules method (MTS-IFRM). In the proposed method, to improve the robustness of the training process of the model, the features of the aerial targets are classified as the input results of the corresponding T-S target recognition model. The intuitionistic fuzzy approach and ridge regression method are used in the consequent identification, which constructs a regression model. To train the premise parameter and reduce the influence of data noise, novel intuitionistic fuzzy C-regression clustering based on dynamic optimization is proposed. Moreover, a modified adaptive weight algorithm is presented to obtain the final outputs, which improves the classification accuracy of the corresponding model. Finally, the experimental results show that the proposed method can effectively recognize the typical aerial targets in error-free and error-prone environments, and that its performance is better than other methods proposed for aerial target recognition.<\/jats:p>","DOI":"10.3390\/rs15245773","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T10:04:47Z","timestamp":1702893887000},"page":"5773","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Source T-S Target Recognition via an Intuitionistic Fuzzy Method"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8661-3988","authenticated-orcid":false,"given":"Chuyun","family":"Zhang","sequence":"first","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Weixin","family":"Xie","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Yanshan","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8947-7021","authenticated-orcid":false,"given":"Zongxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7614","DOI":"10.1007\/s10489-021-02279-5","article-title":"An improved evidence fusion algorithm in multi-sensor systems","volume":"51","author":"Zhao","year":"2021","journal-title":"Appl. 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