Chapter two introduces the concept of entropy in information theory, maximum entropy principle, and minimum cross-entropy principle 第二章首先介绍了信息论中的熵、最大熵原理、最小叉熵原理。
Then the perturbation methods are used to an equivalent nlp problem of minmax problems and the resulted smooth functions are just aggregate functions obtained by maximum entropy principle and minimum cross-entropy principle, respectively 然后文中把这种方法应用到有限维极大极小问题的一个等价非线性规划问题,得到了相应的光滑函数,且与用最大熵原则和最小叉熵原则得到的凝聚函数相同。
Thirdly, a novel parameter-varying adaptive algorithm for rtt and rto estimations based on the information theory and the maximum entropy principle ( mep ) is presented . it is used in the implementation of trinomial protocol to detect packet losses and to adjust the sending rate 再次,给出了一种新的基于信息理论和最大熵原理(mep)的变参数自适应rtt和rto估计算法,在三项式协议实现过程中用于探测丢包及速率调整。
The maximum entropy principle were used to follow population : ( 1 ) mutiallel population in all population that have the given gene distribution, the equilibrium population entropy reach it's maximum, the maximum entropy more than 0, and less than 2 \ nk ( k is the number of the allel ), and maximum entropy equal two times of the same locus gene entropy (1)复等位基因群体对具有同一基因库的复等位基因位点,用最大熵证明了该位点所对应的所有群体中,平衡群体的基因型熵最大,其数值大于等于0,小于等于21nk(这里k为该位点等位基因数目),且等于该位点基因库熵的两倍。