Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/15006
Title: An evolutionary-based approach in RBF neural network training
Authors: Αλεξανδρίδης, Αλέξανδρος Π.
Item type: Conference publication
Conference Item Type: Poster
Keywords: Evolutionary computation;Genetic algorithms;Non-symmetric Fuzzy Means;Radial basis functions;Εξελικτική Υπολογιστική;Γενετικοί Αλγόριθμοι;Ακτινική συνάρτηση βάσης
Subjects: Technology
Electrical engineering
Τεχνολογία
Ηλεκτρολογία Μηχανολογία
Issue Date: 3-Jun-2015
2012
Publisher: IEEE
Abstract: This paper presents a methodology for evolving populations of Radial Basis Function (RBF) networks, in order to optimize the accuracy of the corresponding model predictions. The method encodes possible non-symmetric fuzzy partitions of the input space as chromosomes and then uses the non-symmetric fuzzy means algorithm to deploy an RBF network for each partition. The chromosomes are evolved through the use of a specially designed Genetic Algorithm, thus resulting to improved RBF models. The proposed approach has been applied successfully to neural network training benchmark problems.
Language: English
Citation: Alexandridis, A.P. (2012) An evolutionary-based approach in RBF neural network training, In: Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2012. Madrid, Spain. 17-18 May, 2012. [online]. p. 127-132, 6232817. Available from: http://ieeexplore.ieee.org/
Conference: IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2012
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/15006
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