Ninos, KSarimveis, HAlexandridis, AThis work presents the non-symmetric fuzzy means algorithm which is a new methodology for training Radial Basis Function neural network models. The method is based on a non-symmetric fuzzy partition of the space of input variables which results to networks with smaller structures and better approximation capabilities compared to other state-of-the-art training procedures. The lower modeling error and the smaller size of the produced models become particularly important when they are used in online applications. This is demonstrated by integrating the model produced by the proposed algorithm in a Model Predictive Control configuration, resulting in better control performance and shorter computational times. (C) 2011 Elsevier Ltd. All rights reserved.http://hdl.handle.net/123456789/209438300965-997883710.1016/j.advengsoft.2011.05.026ISI:0002938721000124210engELSEVIER SCI LTDAdvances in Engineering SoftwareRadial basis functionFuzzy partitionModel predictive controlLearning algorithmsRadial basis functionsNeural networksPredictive control systemsRadial basis function networksFuzzy meansMackey-GlassContinuous stirred tank reactorNon-symmetric partitionA Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configurationjournalArticle2011Δημοσίευση σε περιοδικόPublication in journalΛογισμικό (software)Softwareisi - Computer Science, Software Engineering includes resources that are concerned with the programs, routines, and symbolic languages that control the functioning of the hardware and direct its operation. Also covered in this category are computer graphics, digital signal processing, and programming languages.Επιστημονικό άρθροScientific articleΕθνικό Μετσόβιο ΠολυτεχνείοGreek Aggregator OpenArchives.gr | National Documentation Centre (EKT)other