This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a tri...This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a trigger time. For this purpose, we propose the communication-censored distributed learning algorithm for SCN, namely ADMMM-SCN-ET, by introducing the event-triggered communication mechanism to the alternating direction method of multipliers (ADMM). To avoid unnecessary information transmissions, each learning agent is equipped with a trigger function. Only if the event-trigger error exceeds a specified threshold and meets the trigger condition, the agent will transmit the variable information to its neighbors and update its state in time. The simulation results show that the proposed algorithm can effectively reduce the communication cost for training decentralized SCNs and save communication resources.展开更多
Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model structures.Aiming at the above problems,this paper proposes a double pruning st...Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model structures.Aiming at the above problems,this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance.During the training process,the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner,the node pruning rate of each layer is set according to the depth of the DSCN at the current time,the nodes that contribute little to the model are deleted,and the network-related parameters are updated.When the model completes the configuration procedure,the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections;then,the network parameters are updated after pruning is completed.The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed;the model accuracy loss is small,and fine-tuning for accuracy restoration is not needed.The obtained DSCN model has certain application value in the field of regression analysis.展开更多
针对旋转机械失效机理复杂,特征信息差异大,导致的传统诊断模型依赖先验知识,准确率低,适应性差的难题.提出一种基于随机量化数据增强-随机LSTM(Long Short Term Memory)块映射特征提取-随机配置网络(Randomized Quantization-Randomize...针对旋转机械失效机理复杂,特征信息差异大,导致的传统诊断模型依赖先验知识,准确率低,适应性差的难题.提出一种基于随机量化数据增强-随机LSTM(Long Short Term Memory)块映射特征提取-随机配置网络(Randomized Quantization-Randomized LSTM Block Mapping Method-Stochastic Configuration Network,简称RQ-RLBM-SCN)的旋转机械故障诊断方法.首先,为了解决失效机械特征信息小子样,训练样本不足的难题,使用随机量化数据增强将多传感器原始数据样本进行扩充,从而提高模型的适应性、准确率和缓解过拟合问题.其次用随机LSTM块映射方法来提取特征,解决SCN不擅长提取时序数据特征难的问题;然后使用随机配置网络(SCN)进行分类,SCN可以动态配置参数,无需反向传播来更新参数,在保证学习率的同时,还有效的避免梯度爆炸或梯度消失等问题.采用RQ-RLBM-SCN方法能准确识别出轴承和齿轮故障,在10次重复实验中,轴承和齿轮的多传感器数据集上的平均准确率分别达到99.80%、98.75%均高于原始SCN、TSC-SCN、VMD-SCN、SVM和KNN故障诊断方法;该方法可以为建立旋转机械的健康监测模型提供动态方法和诊断思路.展开更多
In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an...In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an optimization control method of gas oxygen content based on model predictive control.First,a stochastic configuration network is utilized to establish a prediction model of gas oxygen content.Second,an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow.Finally,model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions.The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value,which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.展开更多
文摘This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a trigger time. For this purpose, we propose the communication-censored distributed learning algorithm for SCN, namely ADMMM-SCN-ET, by introducing the event-triggered communication mechanism to the alternating direction method of multipliers (ADMM). To avoid unnecessary information transmissions, each learning agent is equipped with a trigger function. Only if the event-trigger error exceeds a specified threshold and meets the trigger condition, the agent will transmit the variable information to its neighbors and update its state in time. The simulation results show that the proposed algorithm can effectively reduce the communication cost for training decentralized SCNs and save communication resources.
基金supported by the National Natural Science Foundation of China(62073006)the Beijing Natural Science Foundation of China(4212032)
文摘Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model structures.Aiming at the above problems,this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance.During the training process,the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner,the node pruning rate of each layer is set according to the depth of the DSCN at the current time,the nodes that contribute little to the model are deleted,and the network-related parameters are updated.When the model completes the configuration procedure,the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections;then,the network parameters are updated after pruning is completed.The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed;the model accuracy loss is small,and fine-tuning for accuracy restoration is not needed.The obtained DSCN model has certain application value in the field of regression analysis.
基金supported by the National Natural Science Foundation of China(62373017,62073006)and the Beijing Natural Science Foundation of China(4212032)。
文摘In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an optimization control method of gas oxygen content based on model predictive control.First,a stochastic configuration network is utilized to establish a prediction model of gas oxygen content.Second,an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow.Finally,model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions.The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value,which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.
