近年来,即时学习软测量方法已被广泛用于过程工业中难测参数的在线估计.然而,常规的即时学习软测量方法仅依靠单一的学习配置,忽略了即时学习性能的多样性,导致预测性能不佳.为此,提出了一种基于多模态扰动的集成即时学习软测量建模方法...近年来,即时学习软测量方法已被广泛用于过程工业中难测参数的在线估计.然而,常规的即时学习软测量方法仅依靠单一的学习配置,忽略了即时学习性能的多样性,导致预测性能不佳.为此,提出了一种基于多模态扰动的集成即时学习软测量建模方法,称为DSS-ELWPLS(Diverse Subspaces and Similarity measures based Ensemble Locally Weighted Partial Least Squares).该方法以局部加权偏最小二乘算法(LWPLS)为基学习器,通过输入特征扰动和相似度扰动以激发即时学习的多样性,然后基于进化多目标优化构建满足多样性和准确性的即时学习基模型.随后采用Stacking集成学习策略,实现即时学习基模型的融合.通过在青霉素发酵过程和工业混炼胶过程中的应用,验证了DSS-ELWPLS方法的有效性和优越性.展开更多
In the context of deep rock engineering,the in-situ stress state is of major importance as it plays an important role in rock dynamic response behavior.Thus,stress initialization becomes crucial and is the first step ...In the context of deep rock engineering,the in-situ stress state is of major importance as it plays an important role in rock dynamic response behavior.Thus,stress initialization becomes crucial and is the first step for the dynamic response simulation of rock mass in a high in-situ stress field.In this paper,stress initialization methods,including their principles and operating procedures for reproducing steady in-situ stress state in LS-DYNA,are first introduced.Then the most popular four methods,i.e.,explicit dynamic relaxation(DR)method,implicit-explicit sequence method,Dynain file method and quasi-static method,are exemplified through a case analysis by using the RHT and plastic hardening rock material models to simulate rock blasting under in-situ stress condition.Based on the simulations,it is concluded that the stress initialization results obtained by implicit-explicit sequence method and dynain file method are closely related to the rock material model,and the explicit DR method has an obvious advantage in solution time when compared to other methods.Besides that,it is recommended to adopt two separate analyses for the whole numerical simulation of rock mass under the combined action of in-situ stress and dynamic disturbance.展开更多
文摘近年来,即时学习软测量方法已被广泛用于过程工业中难测参数的在线估计.然而,常规的即时学习软测量方法仅依靠单一的学习配置,忽略了即时学习性能的多样性,导致预测性能不佳.为此,提出了一种基于多模态扰动的集成即时学习软测量建模方法,称为DSS-ELWPLS(Diverse Subspaces and Similarity measures based Ensemble Locally Weighted Partial Least Squares).该方法以局部加权偏最小二乘算法(LWPLS)为基学习器,通过输入特征扰动和相似度扰动以激发即时学习的多样性,然后基于进化多目标优化构建满足多样性和准确性的即时学习基模型.随后采用Stacking集成学习策略,实现即时学习基模型的融合.通过在青霉素发酵过程和工业混炼胶过程中的应用,验证了DSS-ELWPLS方法的有效性和优越性.
基金Project(41630642)supported by the Key Project of National Natural Science Foundation of ChinaProject(51974360)supported by the National Natural Science Foundation of ChinaProject(2018JJ3656)supported by the Natural Science Foundation of Hunan Province,China。
文摘In the context of deep rock engineering,the in-situ stress state is of major importance as it plays an important role in rock dynamic response behavior.Thus,stress initialization becomes crucial and is the first step for the dynamic response simulation of rock mass in a high in-situ stress field.In this paper,stress initialization methods,including their principles and operating procedures for reproducing steady in-situ stress state in LS-DYNA,are first introduced.Then the most popular four methods,i.e.,explicit dynamic relaxation(DR)method,implicit-explicit sequence method,Dynain file method and quasi-static method,are exemplified through a case analysis by using the RHT and plastic hardening rock material models to simulate rock blasting under in-situ stress condition.Based on the simulations,it is concluded that the stress initialization results obtained by implicit-explicit sequence method and dynain file method are closely related to the rock material model,and the explicit DR method has an obvious advantage in solution time when compared to other methods.Besides that,it is recommended to adopt two separate analyses for the whole numerical simulation of rock mass under the combined action of in-situ stress and dynamic disturbance.
