设计了基于线性加权法的模型求解算法。
The solution algorithm based on linear weighting method is proposed in this paper.
在多目标控制中，线性加权法常常被用于把多目标问题转化为单目标问题的研究中。
In multiple objective control fields, linear weighted method is often used to convert the multiple objective problem to the single objective problem.
通过采用线性加权法、分离部分控制变量法，以及在一定范围内穷举，将问题简化，进而给出模型的求解方法。
We simplify the problems by using the linear weighted method, separate technique of partial control variables, and enumerating in certain range, then give out the solving plan of the model.
然后，利用线性加权法和主要目标法将多目标优化问题转化为单目标优化问题，采用精确算法思想进行模型求解。
Then it USES linear weighting method and main target method to convert the problem of multi-objective optimization into a mono-objective one and works out its solution via exact algorithm.
采用层次分析法设置各层权重，以线性加权法得到各层和谐指数，终得各年和谐度，和谐指数越接近于1，和谐度越高。
Using analytic hierarchy process (AHP) to set the layer weight, in a linear weighting method to get index, the layers of harmony, end up with harmony in each of the degrees.
在选择联盟企业的决策过程中，算法的选择显得尤为重要，本文采用了三种算法：主要目标法、线性加权法和AHP法。
In the process of choosing alliance enterprise, the choice of arithmetic is very important, this article adopts three types of arithmetics: main goal method, linear weighted method, and AHP.
根据弹性薄板大挠度理论，运用加权残值法分析计算幕墙玻璃的挠度和应力，并与幕墙玻璃规范设计进行比较，结果表明，基于非线性理论的设计计算是可行的。
On the basis of elasticity of the large-deflection thin penal, the method of weighted residuals is applied in calculating the stress and deflection of the curtain-wall glass.
采用著名的加权剩余值法和等参数有限单元法，对坝体进行非线性静力分析和非线性有效应力动力分析。
Using the well-known weighted residual method and finite element procedure, a nonlinear static analysis parallel with effective stress dynamic analysis has been made.
作者提出一种更简便的预测方法——加权一元线性回归预测法。
The author proposed a simpler forecast method -weighting single regression linear return forecast method.
针对混沌时间序列的最近邻域预测法，提出了改进的最近邻域点优化选择方法和加权一阶局域线性预测法。
Optimal choice method of the nearest neighboring points and adding weight one-rank local region method is introduced on the nearest neighboring forecasting method of chaotic time series.
本文研究了线性调频信号脉冲压缩的旁瓣抑制问题，分析了频谱加权法和冲激响应加权法的原理。对两种方法的谱平滑效果。
We study the sidelobe suppression for the pulse compression of the LFM signal, and analyze the spectrum weighting and the impulse response weighting method.
在模糊综合评判和灰色聚类评价法中，各个指标间总是采用单一的线性加权的方法。
In fuzzy synthetic evaluation and grey cluster evaluation, linear weighing method is commonly used for dealing with the relationship of each index.
和领域平均法、倒数梯度加权法、纯线性插值法、纯中值滤波法等相比较，其效果改善明显。
We get a better result comparing with mean filter, reciprocal gradient filter, linear filter and median filter.
在传统的线性二次型问题中，一般是通过试错法选择加权矩阵来获得良好的动态响应。
In the classical linear quadratic problems, weighting matrices are usually choosed on trial and error to get good responses.
将该模型应用于对水质评价的实例，得到了比线性加权评价法更令人信服的结果，证明了该模型较加权平均法的优越性。
In comparison with the weighted average model, this method is more satisfactory and furthermore proves the advantages of the model.
在此基础上建立面板数据插值的线性组合模型，提出空间漂移度法确定组合插值模型的加权系数。
A combination interpolation model is constructed by the results of the two models, The weights of the combination model are obtained by information entropy.
利用线性加权和法，建立了评价函数，将多目标优化问题转化为单目标非线性规划问题。
By linear weighing-sum method, the multi-objective optimization was converted into a linear programming problem with the evaluation equation. Process parameters were optimized with SQP algorithm.
采用了层次分析法来确定指标权重，并运用线性加权综合评价法进行竞争力综合排序。
Adopted layer analysis method to determine the weight, and sort order by means of linear-weight comprehensive evaluation.
借助并矢分析，分别采用加权余量法和变分法，推导出了三维非线性各向异性静磁场的矢量位有限元方程的一般计算公式。
The 3d finite element vector potential equations of non-linear anisotropic magnetostatic field have been derived in a pithy style by dyadic analysis.
针对线性调频信号的旁瓣抑制介绍了经典海明窗加权处理方法，对二相编码的旁瓣抑制使用了最优峰值旁瓣电平失配滤波法。
The solution to sidelobe suppression of the LFM and BC signal are discussed in details. For LFM signal, the method of employing Hamming weighting is demonstrated;
针对线性调频信号的旁瓣抑制介绍了经典海明窗加权处理方法，对二相编码的旁瓣抑制使用了最优峰值旁瓣电平失配滤波法。
The solution to sidelobe suppression of the LFM and BC signal are discussed in details. For LFM signal, the method of employing Hamming weighting is demonstrated;
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