The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me...The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.展开更多
Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timel...Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM.展开更多
基金supported by the National Natural Science Foundation of China (No.U1960202).
文摘The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.
文摘Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM.
