Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences
Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. A...
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2021/6455592 |
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doaj-60034af366af42a298e7d3a0f8c1a97d2021-09-20T00:29:31ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52732021-01-01202110.1155/2021/6455592Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering SciencesShivali Chopra0Gaurav Dhiman1Ashutosh Sharma2Mohammad Shabaz3Pratyush Shukla4Mohit Arora5Lovely Professional UniversityGovernment Bikram College of CommerceInstitute of Computer Technology and InformationArba Minch UniversityNew York UniversityLovely Professional UniversityAdaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.http://dx.doi.org/10.1155/2021/6455592 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shivali Chopra Gaurav Dhiman Ashutosh Sharma Mohammad Shabaz Pratyush Shukla Mohit Arora |
spellingShingle |
Shivali Chopra Gaurav Dhiman Ashutosh Sharma Mohammad Shabaz Pratyush Shukla Mohit Arora Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences Computational Intelligence and Neuroscience |
author_facet |
Shivali Chopra Gaurav Dhiman Ashutosh Sharma Mohammad Shabaz Pratyush Shukla Mohit Arora |
author_sort |
Shivali Chopra |
title |
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences |
title_short |
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences |
title_full |
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences |
title_fullStr |
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences |
title_full_unstemmed |
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences |
title_sort |
taxonomy of adaptive neuro-fuzzy inference system in modern engineering sciences |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5273 |
publishDate |
2021-01-01 |
description |
Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields. |
url |
http://dx.doi.org/10.1155/2021/6455592 |
work_keys_str_mv |
AT shivalichopra taxonomyofadaptiveneurofuzzyinferencesysteminmodernengineeringsciences AT gauravdhiman taxonomyofadaptiveneurofuzzyinferencesysteminmodernengineeringsciences AT ashutoshsharma taxonomyofadaptiveneurofuzzyinferencesysteminmodernengineeringsciences AT mohammadshabaz taxonomyofadaptiveneurofuzzyinferencesysteminmodernengineeringsciences AT pratyushshukla taxonomyofadaptiveneurofuzzyinferencesysteminmodernengineeringsciences AT mohitarora taxonomyofadaptiveneurofuzzyinferencesysteminmodernengineeringsciences |
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