ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things
The intersection of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) has garnered ever-increasing attention and research interest. Nevertheless, the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet be...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-09-01
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| Series: | Journal of Automation and Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949855424000327 |
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| author | Jing Nan Wei Dai Chau Yuen Jinliang Ding |
| author_facet | Jing Nan Wei Dai Chau Yuen Jinliang Ding |
| author_sort | Jing Nan |
| collection | DOAJ |
| description | The intersection of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) has garnered ever-increasing attention and research interest. Nevertheless, the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution. Taking advantage of the lightweight constructive neural network (LightGCNet) in developing fast learner models for IIoT, a convex geometric constructive neural network with a low-complexity control strategy, namely, ConGCNet, is proposed in this article via convex optimization and matrix theory, which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet. Firstly, a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process. Secondly, a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate. Finally, the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method. Simulation results, including four benchmark datasets and the real-world ore grinding process, demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate. |
| format | Article |
| id | doaj-art-d8a383d470c9420b9412f5dcce9eb2ae |
| institution | Kabale University |
| issn | 2949-8554 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Automation and Intelligence |
| spelling | doaj-art-d8a383d470c9420b9412f5dcce9eb2ae2025-08-20T03:42:41ZengKeAi Communications Co., Ltd.Journal of Automation and Intelligence2949-85542024-09-013316917510.1016/j.jai.2024.07.004ConGCNet: Convex geometric constructive neural network for Industrial Internet of ThingsJing Nan0Wei Dai1Chau Yuen2Jinliang Ding3China University of Mining and Technology, Xuzhou, 221116, ChinaChina University of Mining and Technology, Xuzhou, 221116, China; Corresponding author.Nanyang Technological University, 50 Nanyang Ave, 639798, SingaporeNortheastern University, Shenyang, 110819, ChinaThe intersection of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) has garnered ever-increasing attention and research interest. Nevertheless, the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution. Taking advantage of the lightweight constructive neural network (LightGCNet) in developing fast learner models for IIoT, a convex geometric constructive neural network with a low-complexity control strategy, namely, ConGCNet, is proposed in this article via convex optimization and matrix theory, which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet. Firstly, a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process. Secondly, a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate. Finally, the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method. Simulation results, including four benchmark datasets and the real-world ore grinding process, demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.http://www.sciencedirect.com/science/article/pii/S2949855424000327Industrial Internet of ThingsLightweight geometric constructive neural networkConvex optimizationResource-constrainedMatrix theory |
| spellingShingle | Jing Nan Wei Dai Chau Yuen Jinliang Ding ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things Journal of Automation and Intelligence Industrial Internet of Things Lightweight geometric constructive neural network Convex optimization Resource-constrained Matrix theory |
| title | ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things |
| title_full | ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things |
| title_fullStr | ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things |
| title_full_unstemmed | ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things |
| title_short | ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things |
| title_sort | congcnet convex geometric constructive neural network for industrial internet of things |
| topic | Industrial Internet of Things Lightweight geometric constructive neural network Convex optimization Resource-constrained Matrix theory |
| url | http://www.sciencedirect.com/science/article/pii/S2949855424000327 |
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