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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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Autores principales: Chopra, Shivali, Dhiman, Gaurav, Sharma, Ashutosh, Shabaz, Mohammad, Shukla, Pratyush, Arora, Mohit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8437605/
https://www.ncbi.nlm.nih.gov/pubmed/34527042
http://dx.doi.org/10.1155/2021/6455592
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author Chopra, Shivali
Dhiman, Gaurav
Sharma, Ashutosh
Shabaz, Mohammad
Shukla, Pratyush
Arora, Mohit
author_facet Chopra, Shivali
Dhiman, Gaurav
Sharma, Ashutosh
Shabaz, Mohammad
Shukla, Pratyush
Arora, Mohit
author_sort Chopra, Shivali
collection PubMed
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.
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spelling pubmed-84376052021-09-14 Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences Chopra, Shivali Dhiman, Gaurav Sharma, Ashutosh Shabaz, Mohammad Shukla, Pratyush Arora, Mohit Comput Intell Neurosci Research Article 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. Hindawi 2021-09-03 /pmc/articles/PMC8437605/ /pubmed/34527042 http://dx.doi.org/10.1155/2021/6455592 Text en Copyright © 2021 Shivali Chopra et al. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Research Article
Chopra, Shivali
Dhiman, Gaurav
Sharma, Ashutosh
Shabaz, Mohammad
Shukla, Pratyush
Arora, Mohit
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences
title 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_short Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences
title_sort taxonomy of adaptive neuro-fuzzy inference system in modern engineering sciences
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8437605/
https://www.ncbi.nlm.nih.gov/pubmed/34527042
http://dx.doi.org/10.1155/2021/6455592
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