To implement a real-time reduction in NOx,a rapid and accurate model is required.A PLS-ELM model based on the combination of partial least squares(PLS)and the extreme learning machine(ELM)for the establishment of the ...To implement a real-time reduction in NOx,a rapid and accurate model is required.A PLS-ELM model based on the combination of partial least squares(PLS)and the extreme learning machine(ELM)for the establishment of the NOx emission model of utility boilers is proposed.First,the initial input variables of the NOx emission model are determined according to the mechanism analysis.Then,the initial input data is extracted by PLS.Finally,the extracted information is used as the input of the ELM model.A large amount of real data was obtained from the distributed control system(DCS)historical database of a 1 000 MW power plant boiler to train and validate the PLS-ELM model.The modeling performance of the PLS-ELM was compared with that of the back propagation(BP)neural network,support vector machine(SVM)and ELM models.The mean relative errors(MRE)of the PLS-ELM model were 1.58%for the training dataset and 1.69%for the testing dataset.The prediction precision of the PLS-ELM model is higher than those of the BP,SVM and ELM models.The consumption time of the PLS-ELM model is also shorter than that of the BP,SVM and ELM models.展开更多
To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal co...To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NO_(x))concentration at the outlet of a selective catalytic reduction(SCR)denitrification system.First,PCA is applied to the feature information extraction of input data,and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NO_(x)concentration at the SCR outlet.Then,the model takes the historical data of the NO_(x)concentration at the SCR outlet as the model input to improve its accuracy.Finally,an optimization algorithm is used to determine the optimal parameters of the model.Compared with the Gaussian process regression,long short-term memory,and convolutional neural network models,the prediction errors are reduced by approximately 78.4%,67.6%,and 59.3%,respectively.The results indicate that the proposed dynamic model structure is reliable and can accurately predict NO_(x)concentrations at the outlet of the SCR system.展开更多
To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data,an effective heterogeneous ensemble of extreme learning machines(HEELM)is proposed.Specifically,the ...To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data,an effective heterogeneous ensemble of extreme learning machines(HEELM)is proposed.Specifically,the kernel extreme learning machine(KELM)and four common extreme learning machine(ELM)models that have different internal activations are contained in the HEELM for enriching the diversity of sub-models.The number of hidden layer nodes of the extreme learning machine is determined by the trial and error method,and the optimal parameters of the kernel extreme learning machine model are determined by cross validation.Moreover,to obtain the best output of the ensemble model,least squares regression is applied to aggregate the outputs of all individual models.Two complex data sets of practical industrial processes are used to test the HEELM performance.The simulation results show that the HEELM has a high prediction accuracy.Compared with the individual ELM models,bagging ELM ensemble model,BP and SVM models,the prediction accuracy of the HEELM model is improved by 4.5%to 8.7%,and the HEELM model can obtain better generalization capability.展开更多
针对传统问答系统答案抽取方式对答案片段的分词和上下文语义理解准确性的依赖严重,抽取过程耗费大量的人力和时间的问题,提出采用分步抽取答案的方法,先从答案片段中抽取包含答案的句子,再从提取的答案句中进行最终答案的抽取方式。在...针对传统问答系统答案抽取方式对答案片段的分词和上下文语义理解准确性的依赖严重,抽取过程耗费大量的人力和时间的问题,提出采用分步抽取答案的方法,先从答案片段中抽取包含答案的句子,再从提取的答案句中进行最终答案的抽取方式。在答案句抽取过程中使用Bi-LSTM(Bi-directional Long Short-Term Memory)和Max Pooling结合的方法构建答案句抽取模型。实验结果表明,在答案句的抽取中,该模型的MRR(Mean Average Precision)指数接近0. 75。展开更多
基金The National Natural Science Foundation of China(No.71471060)Natural Science Foundation of Hebei Province(No.E2018502111)
文摘To implement a real-time reduction in NOx,a rapid and accurate model is required.A PLS-ELM model based on the combination of partial least squares(PLS)and the extreme learning machine(ELM)for the establishment of the NOx emission model of utility boilers is proposed.First,the initial input variables of the NOx emission model are determined according to the mechanism analysis.Then,the initial input data is extracted by PLS.Finally,the extracted information is used as the input of the ELM model.A large amount of real data was obtained from the distributed control system(DCS)historical database of a 1 000 MW power plant boiler to train and validate the PLS-ELM model.The modeling performance of the PLS-ELM was compared with that of the back propagation(BP)neural network,support vector machine(SVM)and ELM models.The mean relative errors(MRE)of the PLS-ELM model were 1.58%for the training dataset and 1.69%for the testing dataset.The prediction precision of the PLS-ELM model is higher than those of the BP,SVM and ELM models.The consumption time of the PLS-ELM model is also shorter than that of the BP,SVM and ELM models.
基金The National Natural Science Foundation of China(No.71471060)the Natural Science Foundation of Hebei Province(No.E2018502111)。
文摘To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NO_(x))concentration at the outlet of a selective catalytic reduction(SCR)denitrification system.First,PCA is applied to the feature information extraction of input data,and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NO_(x)concentration at the SCR outlet.Then,the model takes the historical data of the NO_(x)concentration at the SCR outlet as the model input to improve its accuracy.Finally,an optimization algorithm is used to determine the optimal parameters of the model.Compared with the Gaussian process regression,long short-term memory,and convolutional neural network models,the prediction errors are reduced by approximately 78.4%,67.6%,and 59.3%,respectively.The results indicate that the proposed dynamic model structure is reliable and can accurately predict NO_(x)concentrations at the outlet of the SCR system.
基金The National Natural Science Foundation of China(No.71471060)the Natural Science Foundation of Hebei Province(No.E2018502111)Fundamental Research Funds for the Central Universities(No.2019QN134).
文摘To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data,an effective heterogeneous ensemble of extreme learning machines(HEELM)is proposed.Specifically,the kernel extreme learning machine(KELM)and four common extreme learning machine(ELM)models that have different internal activations are contained in the HEELM for enriching the diversity of sub-models.The number of hidden layer nodes of the extreme learning machine is determined by the trial and error method,and the optimal parameters of the kernel extreme learning machine model are determined by cross validation.Moreover,to obtain the best output of the ensemble model,least squares regression is applied to aggregate the outputs of all individual models.Two complex data sets of practical industrial processes are used to test the HEELM performance.The simulation results show that the HEELM has a high prediction accuracy.Compared with the individual ELM models,bagging ELM ensemble model,BP and SVM models,the prediction accuracy of the HEELM model is improved by 4.5%to 8.7%,and the HEELM model can obtain better generalization capability.
文摘针对传统问答系统答案抽取方式对答案片段的分词和上下文语义理解准确性的依赖严重,抽取过程耗费大量的人力和时间的问题,提出采用分步抽取答案的方法,先从答案片段中抽取包含答案的句子,再从提取的答案句中进行最终答案的抽取方式。在答案句抽取过程中使用Bi-LSTM(Bi-directional Long Short-Term Memory)和Max Pooling结合的方法构建答案句抽取模型。实验结果表明,在答案句的抽取中,该模型的MRR(Mean Average Precision)指数接近0. 75。
