article

A granular-oriented development of functional radial basis function neural networks

Authors:

S. K. OhAuthors Info & Claims

Neurocomputing, Volume 72, Issue 1-3

Pages 420 - 435

https://doi.org/10.1016/j.neucom.2007.12.016

Published: 01 December 2008 Publication History

Abstract

In this study, we develop a design methodology for generalized radial basis function neural networks. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to ''standard'' radial basis function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear or quadratic functions whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low-dimensional synthetic data and selected data coming from the Machine Learning repository.

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Cited By

Kim EOh SPedrycz W(2017)Reinforced rule-based fuzzy modelsKnowledge-Based Systems10.1016/j.knosys.2016.12.003119:C(44-58)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.knosys.2016.12.003
Tsekouras G(2016)Fuzzy rule base simplification using multidimensional scaling and constrained optimizationFuzzy Sets and Systems10.1016/j.fss.2015.10.009297:C(46-72)Online publication date: 15-Aug-2016
https://dl.acm.org/doi/10.1016/j.fss.2015.10.009
Chen HLi TLuo CHorng SWang G(2015)A Decision-Theoretic Rough Set Approach for Dynamic Data MiningIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2014.238787723:6(1958-1970)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1109/TFUZZ.2014.2387877
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Index Terms

A granular-oriented development of functional radial basis function neural networks
1. Computing methodologies

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Published In

cover image Neurocomputing

Neurocomputing Volume 72, Issue 1-3

December, 2008

682 pages

ISSN:0925-2312

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Copyright © Elsevier B.V. © 2008.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2008

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Cited By

Kim EOh SPedrycz W(2017)Reinforced rule-based fuzzy modelsKnowledge-Based Systems10.1016/j.knosys.2016.12.003119:C(44-58)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.knosys.2016.12.003
Tsekouras G(2016)Fuzzy rule base simplification using multidimensional scaling and constrained optimizationFuzzy Sets and Systems10.1016/j.fss.2015.10.009297:C(46-72)Online publication date: 15-Aug-2016
https://dl.acm.org/doi/10.1016/j.fss.2015.10.009
Chen HLi TLuo CHorng SWang G(2015)A Decision-Theoretic Rough Set Approach for Dynamic Data MiningIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2014.238787723:6(1958-1970)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1109/TFUZZ.2014.2387877
Iounousse JEr-Raki SEl Motassadeq AChehouani H(2015)Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite imagesApplied Soft Computing10.1016/j.asoc.2015.01.03730:C(1-13)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.asoc.2015.01.037
Niros ATsekouras GTsolakis DManousakis-Kokorakis AKyriazis D(2015)Hierarchical Fuzzy Clustering in Conjunction with Particle Swarm Optimization to Efficiently Design RBF Neural NetworksJournal of Intelligent and Robotic Systems10.1007/s10846-014-0152-478:1(105-125)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1007/s10846-014-0152-4
Huang WOh SPedrycz W(2014)Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)Neural Networks10.1016/j.neunet.2014.08.00760:C(166-181)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.neunet.2014.08.007
Oh SKim WPedrycz WSeo K(2014)Fuzzy Radial Basis Function Neural Networks with information granulation and its parallel genetic optimizationFuzzy Sets and Systems10.1016/j.fss.2013.08.011237(96-117)Online publication date: 1-Feb-2014
https://dl.acm.org/doi/10.1016/j.fss.2013.08.011
Tsekouras G(2013)LettersNeurocomputing10.1016/j.neucom.2012.11.011108(36-44)Online publication date: 1-May-2013
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Tsekouras GTsimikas J(2013)On training RBF neural networks using input--output fuzzy clustering and particle swarm optimizationFuzzy Sets and Systems10.1016/j.fss.2012.10.004221(65-89)Online publication date: 1-Jun-2013
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Oh SPark HKim WPedrycz W(2013)A new approach to radial basis function-based polynomial neural networksKnowledge and Information Systems10.1007/s10115-012-0551-436:1(121-151)Online publication date: 1-Jul-2013
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