{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:21:53Z","timestamp":1777702913143,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2018,8,28]],"date-time":"2018-08-28T00:00:00Z","timestamp":1535414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,4,11]]},"abstract":"<jats:p>The regular fuzzy neural network (RFNN) is a kind of fuzzy neural network by fuzzifying the feed-forward neural network. The RFNN can directly deal with the language information and it has the merits of fuzzy system and neural network. It is presented a fast learning algorithm based on the extreme learning machine (ELM) for the RFNN in this paper. The RFNN referred here is a three-layer feed-forward fuzzy neural network and the connected weights in the RFNN are all fuzzy numbers. A simulation example is given to approximately realize the fuzzy if-then rules by the RFNN. The results show that the RFNN trained by the proposed algorithm has good performance and approximation ability.<\/jats:p>","DOI":"10.3233\/jifs-18046","type":"journal-article","created":{"date-parts":[[2018,9,4]],"date-time":"2018-09-04T14:51:24Z","timestamp":1536072684000},"page":"3263-3269","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["A fast learning algorithm based on extreme learning machine for regular fuzzy neural network"],"prefix":"10.1177","volume":"36","author":[{"given":"Chunmei","family":"He","sequence":"first","affiliation":[{"name":"College of Information Engineering, Xiangtan University, Xiangtan, China"},{"name":"College of Computer Science and Engineering, NUST, Nanjing, China"}]},{"given":"Yaqi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Xiangtan University, Xiangtan, China"}]},{"given":"Tong","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Xiangtan University, Xiangtan, China"}]},{"given":"Fanhua","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Xiangtan University, Xiangtan, China"}]},{"given":"Yanyun","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Xiangtan University, Xiangtan, China"}]},{"given":"Jinhua","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Xiangtan University, Xiangtan, China"}]}],"member":"179","published-online":{"date-parts":[[2018,8,28]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2004.824250"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(01)00138-1"},{"issue":"19","key":"e_1_3_2_4_2","article-title":"Fuzzy neural network based extreme learning machine technique in credit risk management","volume":"2","author":"Karthekeyan A.R.","year":"2016","unstructured":"KarthekeyanA.R., Fuzzy neural network based extreme learning machine technique in credit risk management, Int J Adv Res Basic Eng Sci Technol2(19) (2016).","journal-title":"Int J Adv Res Basic Eng Sci Technol"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(94)90283-6"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZY.1999.793078"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1999.830838"},{"issue":"2","key":"e_1_3_2_8_2","first-page":"71","article-title":"Overview of fuzzified neural networks with comparison of learning mechanism","volume":"10","author":"Kuo C.Y.","year":"2008","unstructured":"KuoC.Y. and WangH.F., Overview of fuzzified neural networks with comparison of learning mechanism, Int J Fuzzy Syst10(2) (2008), 71\u201383.","journal-title":"Int J Fuzzy Syst"},{"key":"e_1_3_2_9_2","first-page":"560","article-title":"Direct fuzzification of neural networks","volume":"1","author":"Buckley J.J.","year":"1993","unstructured":"BuckleyJ.J. and HayashiY., Direct fuzzification of neural networks, Proc 1st Asian Fuzzy Syst Symp1 (1993), 560\u2013567.","journal-title":"Proc 1st Asian Fuzzy Syst Symp"},{"key":"e_1_3_2_10_2","first-page":"696","article-title":"Fuzzy neural network with fuzzy signals and weights","volume":"2","author":"Hayashi Y.","year":"1992","unstructured":"HayashiY., BuckleyJ.J. and CzogalaE., Fuzzy neural network with fuzzy signals and weights, Proc Int Joint Conf Neural Networks2 (1992), 696\u2013701.","journal-title":"Proc Int Joint Conf Neural Networks"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.227388"},{"key":"e_1_3_2_12_2","first-page":"147","article-title":"Learning of fuzzy neural networks from fuzzy inputs and fuzzy targets","volume":"1","author":"Ishibuchi H.","year":"1993","unstructured":"IshibuchiH., KwonK. and TanakaH., Learning of fuzzy neural networks from fuzzy inputs and fuzzy targets, Proc 5th IFSA World Congr1 (1993), 147\u2013150.","journal-title":"Proc 5th IFSA World Congr"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(94)00281-B"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(95)00060-T"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(99)00070-6"},{"key":"e_1_3_2_16_2","first-page":"189","article-title":"Training fuzzy number neural networks with alpha-cut refinements","volume":"1","author":"Dunyak J.","year":"1997","unstructured":"DunyakJ. and WunschD., Training fuzzy number neural networks with alpha-cut refinements, Proc IEEE Int Conf Syst Man Cybern1 (1997), 189\u2013194.","journal-title":"Proc IEEE Int Conf Syst Man Cybern"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(97)00339-4"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.855916"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(98)00461-8"},{"key":"e_1_3_2_20_2","first-page":"1969","article-title":"Genetic learning algorithms for fuzzy neural nets","volume":"2","author":"Krishnamraju P.V.","year":"1994","unstructured":"KrishnamrajuP.V.et al., Genetic learning algorithms for fuzzy neural nets, Proc IEEE Int Conf Fuzzy Syst2 (1994), 1969\u20131974.","journal-title":"Proc IEEE Int Conf Fuzzy Syst"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2007.07.013"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.08.052"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2016.08.040"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.08.009"},{"key":"e_1_3_2_26_2","first-page":"339","article-title":"An incremental Type-2 Meta-Cognitive extreme learning machine","volume":"47","author":"Pratama M.","year":"2017","unstructured":"PratamaM., ZhangG., ErM.J. and AnavattiS., An incremental Type-2 Meta-Cognitive extreme learning machine, IEEE Trans Cybern47 (2017), 339\u2013353.","journal-title":"IEEE Trans Cybern"},{"key":"e_1_3_2_27_2","first-page":"1","article-title":"A fast learning algorithm for uninorm-based fuzzy neural networks","author":"Lemos A.P.","year":"2012","unstructured":"LemosA.P., CominhasW. and ComideF., A fast learning algorithm for uninorm-based fuzzy neural networks, Annual Meeting of the North American Fuzzy Information Processing Society, 2012, pp. 1\u20136.","journal-title":"Annual Meeting of the North American Fuzzy Information Processing Society"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-18046","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-18046","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-18046","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:37:08Z","timestamp":1777455428000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-18046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,28]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,4,11]]}},"alternative-id":["10.3233\/JIFS-18046"],"URL":"https:\/\/doi.org\/10.3233\/jifs-18046","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,28]]}}}