{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T09:50:27Z","timestamp":1771494627368,"version":"3.50.1"},"reference-count":0,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[1996,11,1]],"date-time":"1996-11-01T00:00:00Z","timestamp":846806400000},"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":[[1996,11]]},"abstract":"<jats:p>\n                    Fuzzy logic systems (FLSs) can be designed using training data (i.e., from M given numerical input\/output pairs) and supervised learning algorithms. Orthogonal least-squares (OLS) learning decomposes an FLS into a linear combination of M\n                    <jats:sub>s<\/jats:sub>\n                    &lt; M nonlinear fuzzy basis functions (FBFs), which are optimized during OLS to match the training data. The drawback to OLS is that the resulting system still contains information from all M initial rules, derived from the training points, even though only the most important M\n                    <jats:sub>s<\/jats:sub>\n                    rules have been established by OLS. This is due to a normalization of the FBFs, and leads to excessive computation times during further processing. Our solution is to construct new FBFs out of the reduced rule base and to run OLS a second time. The resulting system not only is of reduced computational complexity, but is of very similar behavior to the unreduced system. The second run of OLS can be applied to a larger set of training data that greatly improves precision. We illustrate our two-pass OLS algorithm for prediction of the Mackey\u2013Glass chaotic time series. Extensive simulations are provided.\n                  <\/jats:p>","DOI":"10.3233\/ifs-1996-4405","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T17:38:49Z","timestamp":1575308329000},"page":"295-308","source":"Crossref","is-referenced-by-count":3,"title":["Two-Pass Orthogonal Least-Squares Algorithm to Train and Reduce the Complexity of Fuzzy Logic Systems"],"prefix":"10.1177","volume":"4","author":[{"given":"Jorg","family":"Hohensohn","sequence":"first","affiliation":[{"name":"Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA 90089-2564, e-mail: mendel@sipi.usc.edu"}]},{"given":"Jerry M.","family":"Mendel","sequence":"additional","affiliation":[{"name":"Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA 90089-2564, e-mail: mendel@sipi.usc.edu"}]}],"member":"179","published-online":{"date-parts":[[1996,11]]},"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-1996-4405","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-1996-4405","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T08:41:58Z","timestamp":1771490518000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IFS-1996-4405"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1996,11]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[1996,11]]}},"alternative-id":["10.3233\/IFS-1996-4405"],"URL":"https:\/\/doi.org\/10.3233\/ifs-1996-4405","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[1996,11]]}}}