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To overcome the high nonlinearity of the aggregation function of Sigma-if neurons, the training process of the Sigma-if network combines an error backpropagation algorithm with the self-consistency paradigm widely used in physics. But for the same reason, the classical backpropagation delta rule for the MLP network cannot be used. The general equation for the backpropagation generalized delta rule for the Sigma-if neural network is derived and a selection of experimental results that confirm its usefulness are presented.<\/jats:p>","DOI":"10.2478\/v10006-012-0034-5","type":"journal-article","created":{"date-parts":[[2012,6,29]],"date-time":"2012-06-29T14:49:17Z","timestamp":1340981357000},"page":"449-459","source":"Crossref","is-referenced-by-count":30,"title":["Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network"],"prefix":"10.61822","volume":"22","author":[{"given":"Maciej","family":"Huk","sequence":"first","affiliation":[]}],"member":"37438","reference":[{"issue":"3","key":"1","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/0001-6918(82)90043-9","article-title":"Task combination and selective intake of information","volume":"50","author":"D. 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