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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/4757620 |
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| _version_ | 1832562021613174784 |
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| author | Tianye Xing Yidan Wang Yingxue Liu Qi Wu Rong Ma Xiaoling Shang |
| author_facet | Tianye Xing Yidan Wang Yingxue Liu Qi Wu Rong Ma Xiaoling Shang |
| author_sort | Tianye Xing |
| collection | DOAJ |
| 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%. |
| format | Article |
| id | doaj-art-f1883efe0f124feb9f0e3ef862a27efb |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-f1883efe0f124feb9f0e3ef862a27efb2025-02-03T01:23:38ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/4757620An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural NetworkTianye Xing0Yidan Wang1Yingxue Liu2Qi Wu3Rong Ma4Xiaoling Shang5College of Basic MedicineDepartment of Information EngineeringDepartment of Civil EngineeringShangfan School of Culture and Arts Co.Baoding Surveying & Mapping Center for HouseCollege of Basic MedicineAn 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%.http://dx.doi.org/10.1155/2022/4757620 |
| spellingShingle | Tianye Xing Yidan Wang Yingxue Liu Qi Wu Rong Ma Xiaoling Shang An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network Applied Bionics and Biomechanics |
| 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 |
| url | http://dx.doi.org/10.1155/2022/4757620 |
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