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In this paper, the problems of correlation characterizations of complex symmetric matrices are deduced and analyzed, which include the eigenvalue, the condition number and the problem of diagonalization.

本论文分析和阐述了复对称矩阵在相关方面的性质和问题,包括特征值、条件数和对角化等问题。

Much work has been done in the field of bounding eigenvalues and condition numbers, but the support theory is a new conception.

在估计特征值和条件数的界的研究领域,虽然已经有了很多的研究成果,但是支持理论还是一个全新的概念。

Proofs that when iterating with the BP algorithm, if one of the neural units fall into saturation regions at least, and the other one at least don't, then the condition number of the Hessian matrix of the error function is very larger.

证明了BP算法在迭代过程中,如果至少有一个神经元落入饱和区而同时至少有一个没落入饱和区,则目标函数的Hessian阵的条件数变大,这是导致"网络瘫痪"现象的原因,也是BP算法收敛速度慢的主要原因。

Professor Deng pointed out the ill-conditioned problem of grey model firstly, but how to research have not appear. This paper mainly research on the ill-conditioned problem of grey model.

因此研究GM模型的病态性问题,寻找能降低系数矩阵条件数的有效方法,是保证灰预测控制实际效果可靠性的重要基础。

The accurate float ambiguity solutions are obtained using one epoch single frequency phase data. The ameliorated approach can weakens the condition number and improve the precision and efficiency of integer ambiguity solution.

通过改进的正则化矩阵的作用,减弱了法矩阵的病态性,改善了法矩阵的条件数,可以得到更准确的模糊度浮动解,来提高整周模糊度求解的精度和效率。

The inverse of condition number is recommended as a good prior criterion for choosing response positions in least squares approach.

条件数的倒数在最小二乘法响应点的选取时可作为优选的先验判据,归一化残差可作为估算质量的后验判据。

The inverse of condition number is recommended as a good a priori criterion for choosing response positions in least squares approach.

条件数的倒数在最小二乘法响应点的选取时可作为优选的先验判据,归一化残差可作为估算质量的后验判据。

In chapter 2 we make perturbation analysis for the two kinds of nonsingular systems AX=B and systems with multiple right hands involving Kronecker product, and derive normwise , mixed and componentwise condition numbers and their upper bounds respectively.

在第二章中我们对两类非奇异线性方程组AX=B和带Kronecker积的多右端系统进行了扰动分析,并应用矩阵导数得到范数型,混合型和分量型这三种条件数及其上界。

In chapter 3 we further analyze the Moore-Penrose inverse of Kronecker product of A and B and the linear least squares problems involving Kronecker product, then obtain normwise mixed and componentwise condition numbers and the upper bounds similarly.

在第三章中我们进一步在列满秩情形下,对A与的Kronecker积的Moore-Penrose逆和带Kronecker积的线性最小二乘问题做了分析,同样得出了范数型,混合型和分量型这三种条件数及其上界。

Another reason is that the degree of linear independence of the variable basis functions composed by sigmoid functions decreases as the hidden nodes increasing, and this lead up to the inferior matrix condition number during learning, which forces the learning step length to decrease and the convergence speed to slow down.

一是因为未知系统复杂时需要较多的隐节点,从而学习的参数过多减慢了学习速度;另一个原因是随着隐节点数增加,由Sigmoid函数构造的可变基函数线性无关程度减弱,从而导致学习过程中矩阵条件数变差,迫使学习步长减小,收敛速度不得不减慢。

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