利用荣成、海阳两站的自建浮标海温观测数据以及区域大气模式WRF(Weather Research and Forecasting)的气象数值预报数据,基于主成分分析(Principal Component Analysis,PCA)法和长短时记忆(Long Short-Term Memory,LSTM)神经网络,提出...利用荣成、海阳两站的自建浮标海温观测数据以及区域大气模式WRF(Weather Research and Forecasting)的气象数值预报数据,基于主成分分析(Principal Component Analysis,PCA)法和长短时记忆(Long Short-Term Memory,LSTM)神经网络,提出了适用于单站海表温度预报的PCALSTM海温预报模型。该模型可以提供24~120 h预报时效的海温预报,预测效果比数值模型和统计模型明显提高。展开更多
We propose an alternating direction method of multipliers(ADMM)for solving the state constrained optimization problems governed by elliptic equations.The unconstrained as well as box-constrained cases of the Dirichlet...We propose an alternating direction method of multipliers(ADMM)for solving the state constrained optimization problems governed by elliptic equations.The unconstrained as well as box-constrained cases of the Dirichlet boundary control,Robin boundary control,and right-hand side control problems are considered here.These continuous optimization problems are transformed into discrete optimization problems by the finite element method discretization,then are solved by ADMM.The ADMM is an efficient first order algorithm with global convergence,which combines the decomposability of dual ascent with the superior convergence properties of the method of multipliers.We shall present exhaustive convergence analysis of ADMM for these different type optimization problems.The numerical experiments are performed to verify the efficiency of the method.展开更多
文摘利用荣成、海阳两站的自建浮标海温观测数据以及区域大气模式WRF(Weather Research and Forecasting)的气象数值预报数据,基于主成分分析(Principal Component Analysis,PCA)法和长短时记忆(Long Short-Term Memory,LSTM)神经网络,提出了适用于单站海表温度预报的PCALSTM海温预报模型。该模型可以提供24~120 h预报时效的海温预报,预测效果比数值模型和统计模型明显提高。
基金supported by National Natural Science Foundation of China (Grant No. 11471141)the Basic Research of the Science and Technology Development Program of Jilin Province (Grant No. 20150101058JC)
文摘We propose an alternating direction method of multipliers(ADMM)for solving the state constrained optimization problems governed by elliptic equations.The unconstrained as well as box-constrained cases of the Dirichlet boundary control,Robin boundary control,and right-hand side control problems are considered here.These continuous optimization problems are transformed into discrete optimization problems by the finite element method discretization,then are solved by ADMM.The ADMM is an efficient first order algorithm with global convergence,which combines the decomposability of dual ascent with the superior convergence properties of the method of multipliers.We shall present exhaustive convergence analysis of ADMM for these different type optimization problems.The numerical experiments are performed to verify the efficiency of the method.
