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In the iterative, the constant matrixB'and B take the place of variable Jacobian matrix.

这种算法在迭代过程中以不变的系数矩阵和代替变化的雅克比矩阵B'和B'',可以节省存储空间、提高计算速度。

The Least Z-Difference algorithm using Least Trimmed Squares estimator is proposed in this paper.

提出一个使用截尾最小二乘估计的最小高差算法,该算法在迭代过程中通过基于高差直方图的自适应阈值来区分变形区观测量。

Based on analyzing the weighted coefficient matrix of the reconstruction linear model, the convergence speed of ART is enhanced by optimizing the selection order of projections during iteration. The whole performance of the system has been improved. The 12-electrode capacitance tomgraphy system is mounted in the oil/gas/water loop and it is used to monitor oil/gas two-phase flow upwards through vertical pipelines.

为了减少代数重建算法的时间开销,本文从图像重建线性模型的权系数矩阵出发,通过优化各投影在迭代过程中的选取顺序来改善ART算法的收敛特性,使成像系统仅需增加较小的时间开销就可提高系统的成像质量,从而提高了成像系统的整体性能。

In view of convergence of iterative arithmetic, the arithmetic of selfadjusting the linear search directions during iteration was put forward based on practice, which guaranteed fast convergence and robustness of the system.

5对于Newton-Rapson迭代过程,根据实践,提出自动控制和调节收敛因子的算法,使系统具有较高的收敛性和计算稳定性。

A neural network architecture, K-subspaces and time-delay auto-associators, is proposed for phoneme recognition. It extends the phoneme filter neural networks approach by adding linear auto-associators to create p-dimension subspace, and an iteration is employed to improve the decision.

提出了用于音素识别的K子空间和时延自相关器神经网络结构,用将时延设计加入线性自相关器,以扩展音素滤波神经网络的方法,产生p维子空间,并采用迭代过程修改划分,以便捕获语音信号中的时间序列信息。

It is an important method to improve restoration quality by choosing regular parameter according to local feature of images. For example, the variance of pixel brightness in iterative process is a useful feature.

根据图像的局部特征来选取自适应变化的正则化参数是提高图像恢复质量的一个重要方法,可以根据像素在迭代过程中的亮度变化量大小来选取正则化参数。

Many mathematical models in physics, mechanics, biology and astronomy are given in such forms.

大量的物理、力学、生物学以及天文学问题的数学模型都是有连续的和离散的迭代过程描述的。

It was proposed in this paper that the method combined Neural Networks with Maximum Likelihood method was used to solve this inverse problem. It solved the problem of local minima during iteration process, and also improved the precision of estimation.

本文提出了神经网络与极大似然估计相结合的方法,解决了求解上述反演问题中迭代过程发散和陷入局部最优的问题,并且得到了较高的估计精度。

To assure the particle diversity of PSO algorithm effectively, muddleheaded mutation is joined in iteration process.

为有效保证PSO算法的粒子多样性,在迭代过程中加入混沌变异。

Establish the steady-state and transient model using the three hydrodynamics equations (Continuity equation, Momentum equation and Energy equation). By comparing different state equation, it selects the BWRS state equation which is considered the most accurate state equation in current natural gas measurement. It calculates compression factor, density and other Thermal parameters based on BWRS state equation. In Numerical solution of the steady-state and transient model, compression factor, friction coefficient and all the other Thermal parameters are recalculated in each small time step to reduce the numerical calculation error.

在稳态模型的建立上,利用流体力学三大方程(连续性方程、运动方程和能量方程),通过比较不同的状态方程选用了目前被认为最精确的用于天然气计量的BWRS状态方程,并以此方程为基础进行压缩因子、密度等热物性参数的计算;在稳态模型的求解上,选用容易计算,精度较高的标准型龙格—库塔(Runge-Kutta)法进行数值求解,并且在迭代过程的每一小步都重新计算燃气的压缩因子,摩阻系数等所有的计算参数,以减少数值计算的误差。

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