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A Novel Pattern Classification using Granular Reflex Fuzzy Min-Max Neural Network

by Ramesh Kakollu, E. Vargil Vijay
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 19
Year of Publication: 2014
Authors: Ramesh Kakollu, E. Vargil Vijay
10.5120/18862-0562

Ramesh Kakollu, E. Vargil Vijay . A Novel Pattern Classification using Granular Reflex Fuzzy Min-Max Neural Network. International Journal of Computer Applications. 107, 19 ( December 2014), 29-33. DOI=10.5120/18862-0562

@article{ 10.5120/18862-0562,
author = { Ramesh Kakollu, E. Vargil Vijay },
title = { A Novel Pattern Classification using Granular Reflex Fuzzy Min-Max Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 19 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number19/18862-0562/ },
doi = { 10.5120/18862-0562 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:31.641790+05:30
%A Ramesh Kakollu
%A E. Vargil Vijay
%T A Novel Pattern Classification using Granular Reflex Fuzzy Min-Max Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 19
%P 29-33
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Pattern classification is a system for classifying patterns into dissimilar potential categories. The classifier that is used for classification is granular neural network. A granular neural network called granular reflex fuzzy min-max neural network (GrRFMN). GrRFMN uses hyperbox fuzzy set to signify grainy information. Using known data the neural network will be trained, and using this trained neural network data can be classified. Its structural design consists of a spontaneous effect system motivated from human brain to handle group overlies. The GFMN cannot hold data granules of dissimilar sizes professionally. It can be practically done that a convinced quantity of such preprocessing can assist to recover the presentation of a classifier. The GrRFMN is skilled of managing grainy information capably by the training algorithm. The experimental outcomes on valid datasets confirm a good presentation of GRFMN. Experimental results on valid data sets confirm that the GrRFMN can categorize granules of dissimilar granularity further acceptably.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Compensatory neurons grainy information classification granular neural network (GNN) reflex mechanism.