指出在复杂系统建模中，采用多层迭代算法估计模型的参数。
The paper proposes to use parameters of multilayer iterative algorithm estimation model for the model establishent of complex systems.
然而，数据仓库解决方案具有一些重要的不同，包括强大的面向业务的数据、进程的多层迭代以及更多终端用户的涉及。
However, the data warehouse solution has some important differences, including: a strong orientation toward business data, multi-level iteration of the process, and more end-user involvement.
在一阶马尔可夫假设下，利用多层前向神经网络进行迭代逼近求解。
The solution was put forward by the iterative approach through multiple-forward network under Markov hypothesis.
对保温设计的目标函数，采用局部求优，逐步迭代的方法，实现了多层保温经济厚度计算机求解。
Based on the objective function, local optimum and iterative method were adopted to get a computer solution on the optimum economical thickness.
该方法在保证合理计算精度的同时大大降低了迭代过程中矩阵矢量相乘的计算复杂度，提高了多层快速多极子方法计算效率。
It reduces greatly the computational complexity of matrix-vector multiplication in conjugate gradient iteration improves the efficiency of MLFMA while the reasonable accuracy is maintained.
本文阐明了多层理论的基本原理，迭代计算方法，分层数、层厚与计算准确度的关系。
The paper expounds the basic theory of multi-layer analysis, iterative computation method, relations among divided layer quantities, total layer thick and computation precise.
为了求解复合材料多层扁壳在初始薄膜力时的强迫振动，在直接积分法中加入了迭代步骤，推导出了相应的公式和计算步骤。
The iterative method is added into direct integral method to analyze forced vibration problem of laminated composite shallow shells while the relative formulas are derived.
为确定多层人工神经网络的权值和阈值建立了混合求解方法，即迭代前期采用BP算法而迭代后期采用梯度优化法进行计算。
Mixed method is built to calculate the weights and thresholds of multi-layer neural network by utilize BP algorithm and terraced optimizations.
为确定多层人工神经网络的权值和阈值建立了混合求解方法，即迭代前期采用BP算法，而迭代后期采用梯度优化法进行计算。
Mixed method was built to calculate the weights and thresholds of multi-layer neural network by utilize BP algorithm and terraced optimizations.
为确定多层人工神经网络的权值和阈值建立了混合求解方法，即迭代前期采用BP算法，而迭代后期采用梯度优化法进行计算。
Mixed method was built to calculate the weights and thresholds of multi-layer neural network by utilize BP algorithm and terraced optimizations.
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