{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T07:06:48Z","timestamp":1781334408201,"version":"3.54.1"},"reference-count":18,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2011,12,26]],"date-time":"2011-12-26T00:00:00Z","timestamp":1324857600000},"content-version":"vor","delay-in-days":359,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advances in Artificial Neural Systems"],"published-print":{"date-parts":[[2011,1]]},"abstract":"<jats:p>Multilayer perceptron (MLP) with back\u2010propagation learning rule is adopted to predict the winning rates of two teams according to their official statistical data of 2006 World Cup Football Game at the previous stages. There are training samples from three classes: win, draw, and loss. At the new stage, new training samples are selected from the previous stages and are added to the training samples, then we retrain the neural network. It is a type of on\u2010line learning. The 8 features are selected with ad hoc choice. We use the theorem of Mirchandani and Cao to determine the number of hidden nodes. And after the testing in the learning convergence, the MLP is determined as 8\u20102\u20103 model. The learning rate and momentum coefficient are determined in the cross\u2010learning. The prediction accuracy achieves 75% if the draw games are excluded.<\/jats:p>","DOI":"10.1155\/2011\/374816","type":"journal-article","created":{"date-parts":[[2011,12,26]],"date-time":"2011-12-26T21:00:43Z","timestamp":1324933243000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multilayer Perceptron for Prediction of 2006 World Cup Football Game"],"prefix":"10.1155","volume":"2011","author":[{"given":"Kou-Yuan","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai-Ju","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2011,12,26]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/45.535226"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0305\u20100548(99)00003\u20109"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10559\u2010005\u20100098\u20104"},{"key":"e_1_2_8_4_2","first-page":"117","article-title":"The use of neural network technology to model swimming performance","volume":"6","author":"Silva A. 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