伴随向量 meaning in Chinese
adjoint vector
Examples
- In error analysis , we introduce some new thchniques to overcome difficulties in dealing with the combina - tion of the modified method of characteristics - galerkin element procedure ( mmoc - galerkin ) and the mixed finite element
在收敛性分析中采用新的论证方法处理由于特征方法和混合元方法的结合所带来的困难，并得到混合元解及伴随向量函数的最优l ~ 2误差估计。 - We studied the feature representation of synergetic pattern recognition and pointed out that adjoint vector is feature representation of according prototype . furthermore , it represents not only image unique feature but also database comparative feature
论文还对协同特征提取属性进行了深入讨论，指出伴随向量就是原型向量的显著特征，同时包含了图像独有特征和群体的相对特征。 - Piecewise constants are then in the test function space , so mass is cinserved element by element in the discrete level . the optimal l2 error estimate for the unknown function and its following vector and the effect of the parameter e on the convergence order are presented
通过严格的数值分析，我们建立了格式对待求函数及伴随向量的最优l ~ 2误差分析理论，进一步得到收敛阶与小扩散系数之间精致的数量刻划。 - Compared with the other traditional algorithms , our synergetics based classification has the distinguished superiorities in feature extraction layer . the adjoint vectors representing the statistic feature of fingerprint images make global retrieve possible , promote the classification efficiency and deduce the feature extraction difficulty
与传统特征提取过程不同，伴随向量提取了指纹像素域的统计特征，在指纹库中形成整体检索，有效提高了分类效率，降低了特征提取的难度。 - In chapter one , we propose a new mixed method called characteristics mixed finite element method for a convection - dominated diffusion problems with small parameter e : we handle the convection part whth backward difference scheme along the characteristics , obtain much smaller time - trunction errors and avoid numerical dispersion on the front of the peak curve of the flow : we use a lowest order mixed finite element method to deal with the diffusion part , so this scheme can approximate the unknow function and its following vector with high accuracy at the same time
第一章中我们对小参数对流占优扩散问题提出了新的数值方法? ?特征混合有限元方法，即对方程的对流部分采用沿特征线的后退差分格式求解，以保证较小的截断误差限并避免了在流动的锋线前沿数值弥散现象的出现；对流动的扩散部分采用最低次混合元方法求解，以保证格式对未知函数及伴随向量的同时高精度逼近。由于该方法中检验函数可取分片常数，此格式在某种意义上具有局部守恒性质。