adjoint vector meaning in English
伴随向量
Examples
- By solving iterative the sequences , the optimal control law is obtained which consists of analytical linear feed - forward - feedback terms and a nonlinear compensation term , which is the limit of the adjoint vector sequence
通过迭代序列得到的最优抚动抑制控制律由解析的线性前馈反馈项和序列极限形式的非线性补偿项组成。 - 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。 - The characteristic approximation is used to handle the convection part along the direc - tion of fluid namely characteristic direction to ensure the high stability of the method in approximating the sharp fronts and reduce the numerical diffusion ; the mixed finite element spatial approximation is employed to deal with diffusion part and approximate the scalar unknown and the adjoint vector function optimally and simultaneously ; in order to preserve the integral conservation of the method , we introduce the modified characteristic method
该方法对方程的对流部分沿流体流动的方向即特征方向离散以保证格式在流动的锋线前沿逼近的高稳定性，消除数值弥散现象；对方程的扩散部分采用最低次混合有限元方法离散、同时以高精度逼近未知函数及未知函数的梯度；为保证方法的整体守恒性，在格式中引入修正项 - 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。 - The new method is a combination of characteristic approximation to handle the convection part , to ensure the high stability of the method in approximating the sharp fronts and reduce the numerical diffusion , a smaller time truncation is gained at the same time , and a mixed finite element spatial approximation to deal with the diffusion part , the sealer unknown and the adjoint vector function are approximated optimally and simultaneously
此方法即为对方程的对流项沿流体流动的方向即特征方向进行离散，从而保证格式在流动锋线前沿逼近的高稳定性，消除了数值弥散现象，并得到了较小的时间截断误差；另一方面，对方程的扩散项采用混合元离散，可同时高精度逼近未知函数及其伴随向量函数，理论分析表明，此方法是稳定的，具有最优的l ~ 2逼近精度。