Research Article
Crossroads in Classification: Comparison and Analysis of Fuzzy and Neuro-Fuzzy Techniques
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 24 - Issue 2 |
| Published: June 2011 |
| Authors: Apoorvi Sood, Swati Aggarwal |
10.5120/2924-3866
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Apoorvi Sood, Swati Aggarwal . Crossroads in Classification: Comparison and Analysis of Fuzzy and Neuro-Fuzzy Techniques. International Journal of Computer Applications. 24, 2 (June 2011), 13-17. DOI=10.5120/2924-3866
@article{ 10.5120/2924-3866,
author = { Apoorvi Sood,Swati Aggarwal },
title = { Crossroads in Classification: Comparison and Analysis of Fuzzy and Neuro-Fuzzy Techniques },
journal = { International Journal of Computer Applications },
year = { 2011 },
volume = { 24 },
number = { 2 },
pages = { 13-17 },
doi = { 10.5120/2924-3866 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2011
%A Apoorvi Sood
%A Swati Aggarwal
%T Crossroads in Classification: Comparison and Analysis of Fuzzy and Neuro-Fuzzy Techniques%T
%J International Journal of Computer Applications
%V 24
%N 2
%P 13-17
%R 10.5120/2924-3866
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The paper introduces various methods for classification like fuzzy logic, and its combination with artificial neural networks. Datasets from UCI Repository have been used for the implementation of classification models using Matlab 7.0 for Fuzzy Inference System(FIS) and Anfis and Matlab R2007b for Anfis with variable labels and different membership functions.
References
- Ajith Abraham, “Neuro Fuzzy Systems: State-of-the-art Modeling Techniques”.
- Dr. Rajeev Sood, M.ch Uro Surgery, Sr. Uro Surgeon, Dr. R.M.L.Hospital, New Delhi-110001.
- http://archive.ics.uci.edu/ml/datasets.html .
- http://en.wikipedia.org/wiki/Fuzzy_control_system.
- http://en.wikipedia.org/wiki/Fuzzy_logic.
- Jang, “ANFIS: adaptive Network-Based Fuzzy Inference System”, MAY/JUNE 1993, IEEE.
- J.L. Castro and J.M. Zurita, “An Inductive Learning Algorithm In Fuzzy Systems”.
- J.L. Castro, J.J. Castro-Schez, J.M. Zurita, “Learning maximal Structure Rules in Fuzzy Logic for Knowledge Acquisition in expert systems”.
- L.A. Zadeh, Fuzzy sets, Inform. and Control 8 (1965) 338-353.
- Mamdami, E.H.; Assilina, S., "An experiment in linguistic synthesis with a fuzzy logic controller", International Journal of Man-Machine Studies, vol. 7(1), pp. 1-13, 1975.
- http://en.wikipedia.org/wiki/Neural_network.
- http://www.mathworks.com/products/matlab/.
- T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Syst., Man, Cybern., vol. 15, pp. 116-132, 1985.
- Yuanyuan Chai, Limin Jia, and Zundong Zhang, World Academy of Science, Engineering and Technology 51 2009 ,“Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application”.
- Zoran Sevarac, “Neuro Fuzzy Reasoner for Student Modeling”, Department of Information Systems, School of Business Administration FON,University of Belgrade,2006 IEEE.
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