Open Access   Article Go Back

Application of Fuzzy Logic in Neural Network Using Data Mining Techniques: A Survey

J.Sheela Jasmine1

Section:Survey Paper, Product Type: Journal Paper
Volume-4 , Issue-4 , Page no. 333-341, Apr-2016

Online published on Apr 27, 2016

Copyright © J.Sheela Jasmine . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: J.Sheela Jasmine, “Application of Fuzzy Logic in Neural Network Using Data Mining Techniques: A Survey,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.333-341, 2016.

MLA Style Citation: J.Sheela Jasmine "Application of Fuzzy Logic in Neural Network Using Data Mining Techniques: A Survey." International Journal of Computer Sciences and Engineering 4.4 (2016): 333-341.

APA Style Citation: J.Sheela Jasmine, (2016). Application of Fuzzy Logic in Neural Network Using Data Mining Techniques: A Survey. International Journal of Computer Sciences and Engineering, 4(4), 333-341.

BibTex Style Citation:
@article{Jasmine_2016,
author = {J.Sheela Jasmine},
title = {Application of Fuzzy Logic in Neural Network Using Data Mining Techniques: A Survey},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2016},
volume = {4},
Issue = {4},
month = {4},
year = {2016},
issn = {2347-2693},
pages = {333-341},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=942},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=942
TI - Application of Fuzzy Logic in Neural Network Using Data Mining Techniques: A Survey
T2 - International Journal of Computer Sciences and Engineering
AU - J.Sheela Jasmine
PY - 2016
DA - 2016/04/27
PB - IJCSE, Indore, INDIA
SP - 333-341
IS - 4
VL - 4
SN - 2347-2693
ER -

VIEWS PDF XML
1472 1308 downloads 1577 downloads
  
  
           

Abstract

The use of Neuro-Fuzzy Network is extremely wide in data mining due to some trademark like parallel performance, Self-organizing adaptive, power Also, shortcoming tolerance. Data mining models depend on errand they accomplish: Affiliation Rules, Clustering, Prediction, Also, Classification. Neuro-Fuzzy Network is utilized to find outline in data. The gathering of Neuro-Fuzzy Network model Also, data mining strategy can significantly increment the proficiency of data mining Techniques Also, it has been comprehensively used. Diverse Calculations have been discussed or streamlining the Manufactured Neuro-Fuzzy Network (ANN). ANN consolidates with other Calculations to find out the high exact data as compare to Conventional algorithm. The part of ANN utilizing data mining strategies is playing an imperative part in gauging or conjecture about diversions Also, weather. This produces high exact expectations than that of Conventional algorithm. Data mining approaches utilizing ANN can moreover work well. ANN is a highly class calculation which can be accelerated utilizing neuron. The result of which will produce a high speed up ANN. ANN can moreover be utilized or the reason of Evacuating rules from prepared Neuro-Fuzzy networks.

Key-Words / Index Term

ANN; Data mining; Application; Gathering

References

[1] Sheng Zhang; Hong-Xing Liu; Dun-Tang Gao; Wei Wang, “Surveying the methods of improving ANN generalization capability”, Machine Learning and Cybernetics, 2003 International Conference on Year: 2003, Volume: 2 Pages: 1259 – 1263.
[2] Y. X. Jin; B. Xu; X. C. Yang; Z. H. Qin; J. Y. Li; F. Zhao; S. Chen; H. L. Ma; Q. Wu, “Grassland aboveground biomass retrieval from remote sensing data by using artificial neural network in temperate grassland, northern China”, Agro-geoinformatics (Agro-geoinformatics 2014), Third International Conference on Year: 2014 Pages: 1 – 6.
[3] Guozheng Zhang; Faming Zhou; Junfeng Liu; Yong Lan, “Customer Satisfaction Data Analysis Based on BP ANN”, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing Year: 2008 Pages: 1 – 3.
[4] Agustín Gajate; Rodolfo E. Haber; Pastora I. Vega; José. R. Alique, “A Transductive Neuro-Fuzzy Controller: Application to a Drilling Process”, IEEE Transactions on Neural Networks Year: 2010, Volume: 21, Issue: 7 Pages: 1158 – 1167.
[5] L. Arafeh; H. Singh; S. K. Putatunda, “A neuro fuzzy logic approach to material processing”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) Year: 1999, Volume: 29, Issue: 3 Pages: 362 – 370.
[6] H. Ghezelayagh; K. Y. Lee, “Application of neuro-fuzzy identifier for a fossil fuel boiler system”, Power Engineering Society Winter Meeting, 2000. IEEE Year: 2000, Volume: 2 Pages: 1135 – 1139.
[7] P. C. Panchariya; A. K. Palit; D. Popovic; A. L. Sharrna, “Nonlinear system identification using Takagi-Sugeno type neuro-fuzzy model”, Intelligent Systems, 2004. Proceedings. 2004 2nd International IEEE Conference Year: 2004, Volume: 1 Pages: 76 – 81.
[8] Ginalber Serra; Celso Bottura, “An IV-QR Algorithm for Neuro-Fuzzy Multivariable Online Identification”, IEEE Transactions on Fuzzy Systems Year: 2007, Volume: 15, Issue: 2 Pages: 200 – 210.