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ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

by Navneet Walia, Harsukhpreet Singh, Anurag Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 13
Year of Publication: 2015
Authors: Navneet Walia, Harsukhpreet Singh, Anurag Sharma
10.5120/ijca2015905635

Navneet Walia, Harsukhpreet Singh, Anurag Sharma . ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey. International Journal of Computer Applications. 123, 13 ( August 2015), 32-38. DOI=10.5120/ijca2015905635

@article{ 10.5120/ijca2015905635,
author = { Navneet Walia, Harsukhpreet Singh, Anurag Sharma },
title = { ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 13 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number13/22020-2015905635/ },
doi = { 10.5120/ijca2015905635 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:38.048917+05:30
%A Navneet Walia
%A Harsukhpreet Singh
%A Anurag Sharma
%T ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 13
%P 32-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

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Index Terms

Computer Science
Information Sciences

Keywords

ANFIS Fuzzy Logic Takagi-Sugeno (T-S) Model Learning Algorithm

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