ConGCNet:Convex geometric constructive neural network for Industrial Internet of Things  

作　　者：Jing Nan Wei Dai Chau Yuen Jinliang Ding 

机构地区：[1]China University of Mining and Technology,Xuzhou,221116,China [2]Nanyang Technological University,50 Nanyang Ave,639798,Singapore [3]Northeastern University,Shenyang,110819,China

出　　处：《Journal of Automation and Intelligence》2024年第3期169-175,共7页自动化与人工智能（英文）

摘　　要：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.

关 键 词：Industrial Internet of Things Lightweight geometric constructive neural network Convex optimization RESOURCE-CONSTRAINED Matrix theory 

分 类 号：O17[理学—数学]