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By means of preprocessing, i.e., optimal reduced datasets (ORD), the authors make the functional link artificial neural network faster. Optimal reduced datasets, which reduce the whole project base into small subsets that consist of only representative projects. The representative projects are given as input to FLANN and tested on eight state-of-the-art polynomial expansions. The proposed methods are validated on five real time datasets. This approach yields accurate results vis-\u00e0-vis conventional FLANN, support vector machine regression (SVR), radial basis function (RBF), classification, and regression trees (CART).<\/p>","DOI":"10.4018\/jaec.2012040104","type":"journal-article","created":{"date-parts":[[2012,5,16]],"date-time":"2012-05-16T10:07:27Z","timestamp":1337162847000},"page":"62-82","source":"Crossref","is-referenced-by-count":15,"title":["Functional Link Artificial Neural Networks for Software Cost Estimation"],"prefix":"10.4018","volume":"3","author":[{"given":"B. 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