Title Combined projection and kernel basis functions for classification in evolutionary neural networks
Authors Gutiérrez P.A., Hervás C., CARBONERO RUZ, MARIANO, Fernández J.C., CARBONERO RUZ, MARIANO
External publication No
Means Neurocomputing
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 2
JCR Impact 1.44000
SJR Impact 0.47900
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-71649107625&doi=10.1016%2fj.neucom.2008.09.020&partnerID=40&md5=be39885ce8585fe99a47820e794d8d08
Publication date 01/01/2009
ISI 000268733700002
Scopus Id 2-s2.0-71649107625
DOI 10.1016/j.neucom.2008.09.020
Abstract This paper proposes a hybrid neural network model using a possible combination of different transfer projection functions (sigmoidal unit, SU, product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. An evolutionary algorithm is adapted to this model and applied for learning the architecture, weights and node typology. Three different combined basis function models are proposed with all the different pairs that can be obtained with SU, PU and RBF nodes: product-sigmoidal unit (PSU) neural networks, product-radial basis function (PRBF) neural networks, and sigmoidal-radial basis function (SRBF) neural networks; and these are compared to the corresponding pure models: product unit neural network (PUNN), multilayer perceptron (MLP) and the RBF neural network. The proposals are tested using ten benchmark classification problems from well known machine learning problems. Combined functions using projection and kernel functions are found to be better than pure basis functions for the task of classification in several datasets. © 2009 Elsevier B.V.
Keywords Classification (of information); Functions; Learning systems; Multilayer neural networks; Neural networks; Benchmark classification; Evolutionary neural network; Hybrid neural networks; Kernel basis f
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