{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:52:14Z","timestamp":1777704734574,"version":"3.51.4"},"reference-count":0,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[1996,5,1]],"date-time":"1996-05-01T00:00:00Z","timestamp":830908800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[1996,5]]},"abstract":"<jats:p>A new variable step size least mean squares (LMS) algorithm using fuzzy logic (FVSS-LMS) is presented. The change of the step size at each iteration, which increases or decreases according to the degree of misadaption, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed while preserving low misadjustment. The norm of the cross correlation between the estimation error and input data is used as a measure of misadaption. Simulation results are presented to verify the performance of the proposed algorithm.<\/jats:p>","DOI":"10.3233\/ifs-1996-4202","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T17:38:24Z","timestamp":1575308304000},"page":"101-106","source":"Crossref","is-referenced-by-count":1,"title":["A Variable Step Size LMS Algorithm Using Fuzzy Logic"],"prefix":"10.1177","volume":"4","author":[{"given":"Chul-Heui","family":"Lee","sequence":"first","affiliation":[{"name":"Machine Intelligence Institute, Iona College, New Rochelle, NY 10801, e-mail: RRYI@Iona.Bitnet"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald R.","family":"Yager","sequence":"additional","affiliation":[{"name":"Machine Intelligence Institute, Iona College, New Rochelle, NY 10801, e-mail: RRYI@Iona.Bitnet"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[1996,5]]},"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-1996-4202","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-1996-4202","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:53Z","timestamp":1777455713000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IFS-1996-4202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1996,5]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[1996,5]]}},"alternative-id":["10.3233\/IFS-1996-4202"],"URL":"https:\/\/doi.org\/10.3233\/ifs-1996-4202","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[1996,5]]}}}