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1)  reduced entropy
约化熵
1.
The entropy evolution of a pair of atoms interacting with a single-mode vacuum field is investigated by using the quantum reduced entropy,and the influence of the atomic dipole-dipole interaction on the field entropy evolution is discussed.
用量子约化熵研究了双原子与单模真空场相互作用系统的场熵演化,并讨论了原子偶极-偶极相互作用对场熵演化的影响。
2.
The entanglement properties of a single-mode vacuum field interacting with a pair of atoms are investigated by using the quantum reduced entropy,and the influences of the atomic dipole-dipole interaction on the degree of the entanglement are discussed.
用量子约化熵研究了单模真空场与双原子相互作用的纠缠特性,讨论了原子偶极—偶极相互作用对其纠缠度的影响。
2)  quantum reduced entropy
量子约化熵
1.
The quantum reduced entropy evolution of a V-type three-level atom interacting with the two- mode odd-even entangled coherent field is investigated.
运用量子约化熵研究了V型三能级原子与双模奇偶纠缠相干光场相互作用系统中场熵的演化特性。
2.
The entanglement of field-atom and the entangle- ment between the two-mode field are investigated using the quantum reduced entropy and quantum relative entropy,respectively.
研究的系统为双模压缩真空态和玻色-爱因斯坦凝聚体相互作用体系,在双光子跃迁过程中,分别用量子约化熵和量子相对熵研究了该系统中的双模压缩真空态与凝聚体间的纠缠以及双模压缩真空态的模间纠缠,分析了光场初始压缩因子、原子间相互作用对场-凝聚体间纠缠和模间纠缠的影响。
3.
Considering the interaction between two-mode SU(1,1) coherence states and a two-level atom,we investigate the entanglement of field-atom and one of two modes of field using the quantum reduced entropy and quantum relative entropy,respectively.
考虑双模SU( 1,1)相干态与二能级原子相互作用 ,分别用量子约化熵和量子相对熵研究了该系统双模SU( 1,1)相干态与原子间的纠缠以及双模SU( 1,1)相干态的模间纠缠 ,分析了SU( 1,1)双模光子数差和光场失谐量对场—原子纠缠和模间纠缠的影响 。
3)  Von Neumann quantum reduced entropy
Von Neumann量子约化熵
4)  reduced entropy entanglement
约化熵纠缠度
5)  crossentropy constraints
叉熵约束
6)  information entropy restrained
约束信息熵
1.
Bayesian network structure learning is implemented by combining immune genetic algorithm with information entropy restrained on adaptive function looking on expert knowledge as "bacterin",and several Bayesian network structures satisfying the same sample data collection are gained.
把专家知识作为"疫苗",利用免疫遗传算法和约束信息熵适应度函数相结合的方法进行贝叶斯网络结构的学习,得到多个反映同一样本数据集的、网络结构复杂度折衷的、满意的贝叶斯网络结构。
补充资料:公理化方法(见公理化和形式化)


公理化方法(见公理化和形式化)
axiomatical method

  gongllbuafangfa公理化方法化和形式化。(axiomatieal method)见公理
  
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
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