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 n...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.展开更多
The electric energy consumed in every ton of acceptable product, namely energy consumption per ton (ECT), is an important overall index for the production process of a fused magnesium furnace. The furnace is the equip...The electric energy consumed in every ton of acceptable product, namely energy consumption per ton (ECT), is an important overall index for the production process of a fused magnesium furnace. The furnace is the equipment for producing the fused magnesia. The ECT value depends on the current in the smelting process. The optimal operation for a fused magnesium furnace is supposed to have the ECT as low as possible, where the key is to predict ECT accurately. By introducing an unknown high-order non linear term, this paper builds a dynamic ECT model for differe nt production batches based on the static ECT model for one batch. The average current is taken as the input of the dynamic ECT model, which is composed of the unknown high-order nonlinear term and a nonlinear model with unknown parameters. The order of the nonlinear term is determined by the distance correlatio n and the nonlinear term is estimated by the stochastic con figuration n etwork, while the parameters of the non linear model is ide ntified by the least square method. The estimation of the nonli near term alter nates with the parameter identification. This paper proposes a prediction method for ECT, which is composed of the order identification of the non linear term, the alternating identification of the model and the ECT prediction model. The simulation experiments are conducted by the on-site data, and the results verify the effectiveness of the proposed prediction method.展开更多
文摘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.
基金National Natural Science Foundation of China (Nos. 61525302, 61590922, 61503066, 61533007)in part by the Project of Industry and Information Technology Ministry (No. 20171122-6)in part by the Projects of Shenyang (No. Y17-0-004).
文摘The electric energy consumed in every ton of acceptable product, namely energy consumption per ton (ECT), is an important overall index for the production process of a fused magnesium furnace. The furnace is the equipment for producing the fused magnesia. The ECT value depends on the current in the smelting process. The optimal operation for a fused magnesium furnace is supposed to have the ECT as low as possible, where the key is to predict ECT accurately. By introducing an unknown high-order non linear term, this paper builds a dynamic ECT model for differe nt production batches based on the static ECT model for one batch. The average current is taken as the input of the dynamic ECT model, which is composed of the unknown high-order nonlinear term and a nonlinear model with unknown parameters. The order of the nonlinear term is determined by the distance correlatio n and the nonlinear term is estimated by the stochastic con figuration n etwork, while the parameters of the non linear model is ide ntified by the least square method. The estimation of the nonli near term alter nates with the parameter identification. This paper proposes a prediction method for ECT, which is composed of the order identification of the non linear term, the alternating identification of the model and the ECT prediction model. The simulation experiments are conducted by the on-site data, and the results verify the effectiveness of the proposed prediction method.
