ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things Article Swipe
Jing Nan
,
Wei Dai
,
Chau Yuen
,
Jinliang Ding
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jai.2024.07.004
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jai.2024.07.004
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.
Related Topics
Concepts
Computer science
Constructive
Mathematical optimization
Artificial neural network
Computational complexity theory
Benchmark (surveying)
Convergence (economics)
Rate of convergence
Convex optimization
Piecewise
Process (computing)
Artificial intelligence
Regular polygon
Algorithm
Mathematics
Economics
Economic growth
Geodesy
Geometry
Geography
Computer network
Mathematical analysis
Channel (broadcasting)
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jai.2024.07.004
- OA Status
- diamond
- Cited By
- 4
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400849744
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- OpenAlex ID
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https://openalex.org/W4400849744Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jai.2024.07.004Digital Object Identifier
- Title
-
ConGCNet: Convex geometric constructive neural network for Industrial Internet of ThingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-20Full publication date if available
- Authors
-
Jing Nan, Wei Dai, Chau Yuen, Jinliang DingList of authors in order
- Landing page
-
https://doi.org/10.1016/j.jai.2024.07.004Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.jai.2024.07.004Direct OA link when available
- Concepts
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Computer science, Constructive, Mathematical optimization, Artificial neural network, Computational complexity theory, Benchmark (surveying), Convergence (economics), Rate of convergence, Convex optimization, Piecewise, Process (computing), Artificial intelligence, Regular polygon, Algorithm, Mathematics, Economics, Economic growth, Geodesy, Geometry, Geography, Computer network, Mathematical analysis, Channel (broadcasting), Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
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- Related works (count)
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10Other works algorithmically related by OpenAlex
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