A Novel Fuzzy Rough Granular Neural Network for Classification

dc.contributor.author	Ganivada, Avatharam
dc.contributor.author	Pal, Sankar K.
dc.date.accessioned	2022-03-27T05:54:14Z
dc.date.available	2022-03-27T05:54:14Z
dc.date.issued	2011-09-01
dc.description.abstract	A novel fuzzy rough granular neural network (NFRGNN) based on the multilayer perceptron using back-propagation algorithm is described for fuzzy classification of patterns. We provide a development strategy of knowledge extraction from data using fuzzy rough set theoretic techniques. Extracted knowledge is then encoded into the network in the form of initial weights. The granular input vector is presented to the network while the target vector is provided in terms of membership values and zeros. The superiority of NFRGNN is demonstrated on several real life data sets. © 2011 Copyright Taylor and Francis Group, LLC.
dc.identifier.citation	International Journal of Computational Intelligence Systems. v.4(5)
dc.identifier.issn	18756891
dc.identifier.uri	10.1080/18756891.2011.9727852
dc.identifier.uri	http://www.tandfonline.com/doi/abs/10.1080/18756891.2011.9727852
dc.identifier.uri	https://dspace.uohyd.ac.in/handle/1/8697
dc.subject	fuzzy pattern classification
dc.subject	fuzzy reflexive relation
dc.subject	fuzzy rough sets
dc.subject	Granular computing
dc.subject	rule based layered network
dc.title	A Novel Fuzzy Rough Granular Neural Network for Classification
dc.type	Journal. Article
dspace.entity.type

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