研究了不精确牛顿法的局部收敛性态。
在有限元分析模型中引入了牛顿迭代法，以使每一时间步长的末端温度满足某一限制条件而平衡收敛。
Newton Raphson method was introduced into the FEM analysis model in order to ensure that the solution of each iterative step would converge by means of satisfying some restrictive condition.
保留非线性潮流算法是为了改进牛顿法在处理病态条件时的缺陷，提高收敛性能而提出的。
The retaining-nonlinearity algorithm was presented in order to ameliorate the limitation left by Newton-Raphson who dealt with morbidity condition, and thus improved the astringency.
研究了一类超定非线性方程组的牛顿迭代法的收敛性。
The convergence properties of Newton's method for a type of overdetermined systems of equations were studied.
针对牛顿迭代法收敛精度和速度受初值影响的问题，基于数据插值和拟合方法，研究了迭代初值生成技术。
Meanwhile, considering that Newton iterative method is sensitive to the initial value of parameters, the paper studied generated methods for initial values on basis of data interpolation and fitting.
在已有的基础上探讨了它的半局部收敛性，利用强函数原理，在一定的条件下给出并证明不精确牛顿法的半局部收敛性。
There have many papers for its local convergence, This paper probes into the semi-local convergence using a majorant function principle on some weak condition.
计算熵密度函数时采用牛顿迭代法，从而解决了在分析基桩竖向承载力的可靠度时可能产生的迭代不收敛的情况。
The Newton iterative method is used in the calculation of entropy density function, by which the non-convergence issue in the calculation for the vertical bearing capacity of piles is solved.
牛顿迭代法也称为牛顿切线法，是解非线性方程的一种方法，通过实例对该方法进行了介绍，包括其理论依据、误差估计、收敛阶数、迭代法初始值的选取规则等。
This paper introduces the method with examples to explain it, including its connective knowledge, theory bases, error estimation, convergence order, and the choosing rule for starting value of it.
与拟牛顿法和蚁群算法相比，新算法不仅提高了解的精确性，而且增强了收敛的可靠性。
Compared with the quasi Newton methods and ACA, the solution accuracy of new algorithm is not only improved, but also the convergent reliability is increased.
牛顿迭代法的收敛性是极易受到初始值或初始猜测值的影响。
Convergence of the Newton Iterative Method is highly sensitive to the initialization or initial guess.
该算法通过两步递 归最优化方法来实现 ，并采用改进的高斯—牛顿法来确保算法的快速收敛 性。
The EML registration is achieved by two step recursive optimization. The quick convergence is assured through the improved Gauss Newton algorithm.
该算法通过两步递 归最优化方法来实现 ，并采用改进的高斯—牛顿法来确保算法的快速收敛 性。
The EML registration is achieved by two step recursive optimization. The quick convergence is assured through the improved Gauss Newton algorithm.
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