{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T17:16:52Z","timestamp":1695316612788},"reference-count":0,"publisher":"Sciedu Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIR"],"abstract":"<jats:p>The paper deals with a novel algorithm used to improve identification quality of clusters generated by predictive clustering\u00a0algorithm as a tool to identify states preceding to bifurcations for a system governed by von Karman equations. To construct\u00a0bifurcation precursors, solutions (of the equations) observed on bifurcation paths are clustered; centers of the clusters constitute a\u00a0set of bifurcation precursors. To decrease identification error rate, quality of each precursor is assessed with the employment of\u00a0an additional, validation set. The paper concerns with two approaches to this procedure; the first one employs a single number\u00a0to assess identification value of a cluster in order to delete those with low identification values. The second approach uses\u00a0proposed knowledge extraction procedure to ascertain rules of replacement of the precursors chosen by the algorithm (active) by\u00a0more efficient one. A wide-ranging simulation reveals that the best variant (provided that the Wishart clustering algorithm is\u00a0utilized) is the replacement of the active cluster in conjunction local normalization of data. The optimal parameters values for\u00a0both algorithms, arriving at essentially decreased identification errors.<\/jats:p>","DOI":"10.5430\/air.v6n2p51","type":"journal-article","created":{"date-parts":[[2017,5,1]],"date-time":"2017-05-01T22:54:18Z","timestamp":1493679258000},"page":"51","source":"Crossref","is-referenced-by-count":2,"title":["Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations"],"prefix":"10.5430","volume":"6","author":[{"given":"Vasilii A.","family":"Gromov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Igor M.","family":"Voronin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladislav R.","family":"Gatylo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evgenii T.","family":"Prokopalo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3394","published-online":{"date-parts":[[2017,5,1]]},"container-title":["Artificial Intelligence Research"],"original-title":[],"link":[{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/viewFile\/11002\/7063","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/viewFile\/11002\/7063","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,5,1]],"date-time":"2017-05-01T22:54:18Z","timestamp":1493679258000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/view\/11002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,1]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2017,2,27]]}},"URL":"https:\/\/doi.org\/10.5430\/air.v6n2p51","relation":{},"ISSN":["1927-6982","1927-6974"],"issn-type":[{"value":"1927-6982","type":"electronic"},{"value":"1927-6974","type":"print"}],"subject":[],"published":{"date-parts":[[2017,5,1]]}}}