The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determine...The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determined with Lahitte criterion;then,the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data,and the factors of water pressure,temperature and time effect are considered in the models;finally,according to the monitoring data from 2006 to 2020 of five typical measuring points including J23(on dam section 24^(#)),J33(on dam section 4^(#)),J35(on dam section 8^(#)),J37(on dam section 12^(#)),and J39(on dam section 15^(#))located on the crest of Wuqiangxi concrete gravity dam,the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models.A deep learning model is developed based on long short-term memory(LSTM)recurrent neural network.In the LSTM model,two LSTMlayers are used,the rectified linear unit function is adopted as the activation function,the input sequence length is 20,and the random search is adopted.The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set,and the monitoring data from 2018 to 2020 are taken as the test set.From the results of case study,we can find that(1)the good fitting results can be obtained with the two statistical models;(2)the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors;(3)the prediction accuracy of the LSTM model increases with increasing the amount of training data.In the deformation prediction of concrete gravity dam,the LSTM model is suggested when there are sufficient training data,while the partial least squares regression method is suggested when the training data are insufficient.展开更多
Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent var...Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.展开更多
A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares(ARX-PLS) framework is proposed to tackle the high dimension, coupled and constraints problems in industr...A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares(ARX-PLS) framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restriction. ARX-PLS decoupling character enables to turn the multivariable model predictive control(MPC) controller design in original space into the multi-loop single input single output(SISO) MPC controllers design in latent space.An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control(IMC) method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.展开更多
对年用电量的预测若采用一般最小二乘回归法建模,其估计参数存在着很大的误差且物理意义明显不足。而偏最小二乘回归方法则实现了多元线性回归、主成分分析和典型相关分析的综合、克服了自变量之间的多重相关性的问题,因而更具有先进性...对年用电量的预测若采用一般最小二乘回归法建模,其估计参数存在着很大的误差且物理意义明显不足。而偏最小二乘回归方法则实现了多元线性回归、主成分分析和典型相关分析的综合、克服了自变量之间的多重相关性的问题,因而更具有先进性,其计算结果更为可靠,在实际系统中的可解释性也更强,且方法简单,计算快捷。该文将偏最小二乘回归模型(Partial Least Square Regression,PLS)应用于年用电量预测,并与基于最小二乘的多元线性回归模型预测成果进行对比,探讨了偏最小二乘法在电力负荷预测中的可行性和优势。通过四川省电网年用电量预测表明:偏最小二乘回归法比一般最小二乘法优,具有较强的实用性。展开更多
文摘The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determined with Lahitte criterion;then,the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data,and the factors of water pressure,temperature and time effect are considered in the models;finally,according to the monitoring data from 2006 to 2020 of five typical measuring points including J23(on dam section 24^(#)),J33(on dam section 4^(#)),J35(on dam section 8^(#)),J37(on dam section 12^(#)),and J39(on dam section 15^(#))located on the crest of Wuqiangxi concrete gravity dam,the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models.A deep learning model is developed based on long short-term memory(LSTM)recurrent neural network.In the LSTM model,two LSTMlayers are used,the rectified linear unit function is adopted as the activation function,the input sequence length is 20,and the random search is adopted.The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set,and the monitoring data from 2018 to 2020 are taken as the test set.From the results of case study,we can find that(1)the good fitting results can be obtained with the two statistical models;(2)the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors;(3)the prediction accuracy of the LSTM model increases with increasing the amount of training data.In the deformation prediction of concrete gravity dam,the LSTM model is suggested when there are sufficient training data,while the partial least squares regression method is suggested when the training data are insufficient.
文摘Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.
基金Supported by the National Natural Science Foundation of China （61174114, 60574047）, the National High Technology Re-search and Development Program of China （2007AA04Z168） and the Research Fund for the Doctoral Program of Higher Education of China （20120101130016）.
文摘A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares(ARX-PLS) framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restriction. ARX-PLS decoupling character enables to turn the multivariable model predictive control(MPC) controller design in original space into the multi-loop single input single output(SISO) MPC controllers design in latent space.An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control(IMC) method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.
文摘对年用电量的预测若采用一般最小二乘回归法建模,其估计参数存在着很大的误差且物理意义明显不足。而偏最小二乘回归方法则实现了多元线性回归、主成分分析和典型相关分析的综合、克服了自变量之间的多重相关性的问题,因而更具有先进性,其计算结果更为可靠,在实际系统中的可解释性也更强,且方法简单,计算快捷。该文将偏最小二乘回归模型(Partial Least Square Regression,PLS)应用于年用电量预测,并与基于最小二乘的多元线性回归模型预测成果进行对比,探讨了偏最小二乘法在电力负荷预测中的可行性和优势。通过四川省电网年用电量预测表明:偏最小二乘回归法比一般最小二乘法优,具有较强的实用性。
