We propose a model-calibrated K-L relative entropy minimization (MKLEM) approach to using complete auxiliary information from survey data.
本文我们提出了使用调查数据中完全辅助信息的模型校正K-L相对熵最小化方法。
At present, entropy generation minimization and potential capacity dissipation extremum are the two principles to value heat transfer performance.
目前对传热效果的评价存在熵产最小化和势容耗散取得极值两种不同的准测。
The off-lattice model is adopted and the relative entropy is used as a minimization function to predict the tertiary structure of a protein.
采用非格点模型，以相对熵作为优化函数，进行蛋白质三维结构预测。
The numerical and analytic results show that the heat transfer rate obtained by potential capacity dissipation minimization is better than that by entropy generation minimization.
数值计算和理论分析的结果表明，根据最小传递势容耗散原理得到的结果优于最小熵产原理得到的结果。
The numerical and analytic results show that the heat transfer rate obtained by potential capacity dissipation minimization is better than that by entropy generation minimization.
数值计算和理论分析的结果表明，根据最小传递势容耗散原理得到的结果优于最小熵产原理得到的结果。
应用推荐