1) generalized XOR
广义异或
1.
The computational results on the generalized XOR problem show that the proposed method is superior to original neural network ensemble.
对广义异或问题的仿真计算表明,该方法的性能优于标准的神经网络集成方法。
3) Generalized Singularity
广义奇异性
4) generalized singular value decomposition
广义奇异值分解
1.
Then,by the generalized singular value decomposition,a general symmetric solution of the minimum residual problem is obtained.
首先,分别给出了与矩阵最小剩余问题及其最优近似问题等价的线性方程;其次,用广义奇异值分解得到了与最小剩余问题等价的线性方程的对称解,即最小剩余问题的对称解;最后,通过寻求与最优近似问题等价的线性方程的对称解,从而得到了矩阵的最优近似问题的最优近似解。
2.
The necessary and sufficient conditions for the solvability of such solutions are derived by using the generalized singular value decomposition.
利用广义奇异值分解,导出了矩阵方程(A*XA,B*XB)=(C,D)有Hermite部分是半正定的解、Hermite半正定的解的充分必要条件,同时给出了解的通式。
3.
By applying the singular value decomposition(SVD) and the generalized singular value decomposition(QSVD),the sufficient and necessary conditions and the normal solutions for the inverse problem of anti-centrosymmetric matrix with a sub-matrix constraint are given,and the optimal approximate solution is obtained.
利用矩阵奇异值分解以及矩阵对的广义奇异值分解,给出了子矩阵约束下反中心对称矩阵反问题有解的充要条件及其通解表达式,并得到了最佳逼近解。
5) generalized singular-value decomposition
广义奇异值分解
1.
Acquires the least squares solutions of the matrix equation AXB=E,CXD=F by constructing the normal equation of the matrix equation and applying the generalized singular-value decomposition of coefficient matrices.
借助于矩阵方程AXB=E,CXD=F的正规方程及系数矩阵的广义奇异值分解,得到了此矩阵方程的最小二乘解。
2.
The necessary and sufficient conditions for the consistency of the equations with an anti-symmetric condition on solutions are derived using the full-rank decomposition,generalized inverse and generalized singular-value decomposition.
分别用满秩分解、广义逆和广义奇异值分解导出了具有反对称解的充分必要条
6) generalized anomeric effect
广义异头效应
补充资料：广义
范围较宽的定义（跟‘狭义’相对）：～的杂文也可以包括小品文在内。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条