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An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network

An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method...

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Detalles Bibliográficos
Autores principales: Xing, Tianye, Wang, Yidan, Liu, Yingxue, Wu, Qi, Ma, Rong, Shang, Xiaoling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410954/
https://www.ncbi.nlm.nih.gov/pubmed/36032049
http://dx.doi.org/10.1155/2022/4757620
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author Xing, Tianye
Wang, Yidan
Liu, Yingxue
Wu, Qi
Ma, Rong
Shang, Xiaoling
author_facet Xing, Tianye
Wang, Yidan
Liu, Yingxue
Wu, Qi
Ma, Rong
Shang, Xiaoling
author_sort Xing, Tianye
collection PubMed
description An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. Injury Assessment. The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%.
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spelling pubmed-94109542022-08-26 An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network Xing, Tianye Wang, Yidan Liu, Yingxue Wu, Qi Ma, Rong Shang, Xiaoling Appl Bionics Biomech Research Article An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. Injury Assessment. The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%. Hindawi 2022-08-18 /pmc/articles/PMC9410954/ /pubmed/36032049 http://dx.doi.org/10.1155/2022/4757620 Text en Copyright © 2022 Tianye Xing 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
Xing, Tianye
Wang, Yidan
Liu, Yingxue
Wu, Qi
Ma, Rong
Shang, Xiaoling
An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network
title An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network
title_full An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network
title_fullStr An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network
title_full_unstemmed An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network
title_short An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network
title_sort intelligent health monitoring model based on fuzzy deep neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410954/
https://www.ncbi.nlm.nih.gov/pubmed/36032049
http://dx.doi.org/10.1155/2022/4757620
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