{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T11:59:47Z","timestamp":1761393587131},"reference-count":14,"publisher":"Wiley","issue":"13","license":[{"start":{"date-parts":[[2007,3,21]],"date-time":"2007-03-21T00:00:00Z","timestamp":1174435200000},"content-version":"vor","delay-in-days":5923,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems &amp;amp; Computers in Japan"],"published-print":{"date-parts":[[1991,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Because of its high performance, the learning vector quantization (LVQ) proposed by Kohonen is noteworthy as a method for realizing a neural network. We propose herein a new method of LVQ using fuzzy theory and call it \u201cfuzzy learning vector quantization\u201d (FLVQ).<\/jats:p><jats:p>FLVQ algorithm is as simple as that of LVQ, and its capability of pattern recognition is higher than that of a conventional neural network. Although it is difficult for conventional neural networks to discriminate an input pattern of an unknown category from those of known ones, FLVQ can do it. Since reference vectors of FLVQ are described by use of fuzzy sets and their membership functions are obtained from learning, the data features can be effectively extracted using FLVQ.<\/jats:p><jats:p>We have used FLVQ in an odor pattern recognition system and compared its capability with those of conventional neural networks. As a result, it was confirmed that FLVQ had higher ability in an odor discrimination from the known one than is the case in conventional networks and that an unknown odor was discriminated by FLVQ.<\/jats:p>","DOI":"10.1002\/scj.4690221310","type":"journal-article","created":{"date-parts":[[2007,7,7]],"date-time":"2007-07-07T20:26:20Z","timestamp":1183839980000},"page":"93-103","source":"Crossref","is-referenced-by-count":15,"title":["New method of learning vector quantization using fuzzy theory"],"prefix":"10.1002","volume":"22","author":[{"given":"Yuuichi","family":"Sakuraba","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takamichi","family":"Nakamoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toyosaka","family":"Moriizumi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2007,3,21]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/5236.001.0001"},{"key":"e_1_2_1_3_2","unstructured":"L.Cooper.Adaptive pattern recognition. In: Tutorial in IJCNN'89 (1989)."},{"key":"e_1_2_1_4_2","volume-title":"Self\u2010Organization and Associative Memory","author":"Kohonen T.","year":"1987"},{"key":"e_1_2_1_5_2","first-page":"61","article-title":"Statistical pattern recognition with neural networks: Benchmarking studies","author":"Kohonen T.","year":"1987","journal-title":"Proc. of ICNN"},{"key":"e_1_2_1_6_2","first-page":"31","article-title":"Shift\u2010tolerant, multiphoneme recognition using learning vector quantization","volume":"88","author":"McDermott E.","year":"1988","journal-title":"IECI Technical Report"},{"key":"e_1_2_1_7_2","first-page":"65","article-title":"Learning in an LVQ\u2010based phoneme recognition system","volume":"88","author":"Yokota M.","year":"1988","journal-title":"IECI Technical Report"},{"key":"e_1_2_1_8_2","first-page":"49","article-title":"Phoneme recognition using HMMs with an LVQ codebook","volume":"89","author":"Iwamida H.","year":"1989","journal-title":"IECI Technical Report"},{"key":"e_1_2_1_9_2","first-page":"33","article-title":"Phoneme recognition using LVQ2","volume":"89","author":"Endo M.","year":"1989","journal-title":"IECI Technical Report"},{"key":"e_1_2_1_10_2","first-page":"75","article-title":"Systematic explanation of learning vector quantization and multilayer perceptron","volume":"88","author":"Katagiri S.","year":"1988","journal-title":"IECI Technical Report"},{"key":"e_1_2_1_11_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0140525X00047336"},{"issue":"7","key":"e_1_2_1_12_2","first-page":"33","article-title":"Odor sensor using neural network","volume":"58","author":"Nakamoto T.","year":"1989","journal-title":"The Japan Society of Applied Physics"},{"key":"e_1_2_1_13_2","first-page":"61","article-title":"Proposal of learning vector quantization method using fuzzy theory","volume":"89","author":"Sakuraba Y.","year":"1990","journal-title":"IECE Technical Report"},{"key":"e_1_2_1_14_2","unstructured":"M.Sugeno. Fuzzy control. Nikkan Kougyou Shinbunsya (1988)."},{"key":"e_1_2_1_15_2","first-page":"13","article-title":"Improvement of whisky\u2010aroma identification system","volume":"90","author":"Fukuda A.","year":"1990","journal-title":"IECE Technical Report"}],"container-title":["Systems and Computers in Japan"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fscj.4690221310","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/scj.4690221310","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T03:15:56Z","timestamp":1698030956000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/scj.4690221310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1991,1]]},"references-count":14,"journal-issue":{"issue":"13","published-print":{"date-parts":[[1991,1]]}},"alternative-id":["10.1002\/scj.4690221310"],"URL":"https:\/\/doi.org\/10.1002\/scj.4690221310","archive":["Portico"],"relation":{},"ISSN":["0882-1666","1520-684X"],"issn-type":[{"value":"0882-1666","type":"print"},{"value":"1520-684X","type":"electronic"}],"subject":[],"published":{"date-parts":[[1991,1]]}}}