提出了概念-权向量组匹配算法。
The paper proposes concepts-weights vectors team matching algorithm.
提出了一种计算自适应方向图权向量的迭代算法。
In this paper, an iterative algorithm is presented to compute weight vector of adaptive pattern.
最后，分析文献中关于状态变权向量的两种改进定义之间的关系。
Finally, relationships between two improved definitions of state variable weight vectors given in related literatures are analyzed.
利用导频符号进行权向量更新算法，并作为空间波束成型器的参考信号。
The pilot symbols were used to estimate channel and acted as reference signal of beam-former.
模型由于引入指标权向量，使其更符合实际情况，以适用于化工系统中。
The weighting vector of indexes is introduced into the models to enable them to match actual situations and have to be applied to studies chemical engineering.
算法利用输出信号和参考信号之间“几何功率”误差的最小化来求解最优权向量。
This new beamformer minimizes the "geometric power" error between the beamformer s output and the reference signal.
通过分析一类特殊的状态变权向量，进一步揭示了状态变权向量与均衡函数之间的内在联系。
Then via the analysis of a special state variable weight vector, an essential relation between state variable weight vector and balance function is interpreted further.
其它频率点上满足宽带恒定束宽要求的复权向量可以根据参考频率上的复数权值通过折算求得。
The complex weighting vectors at other frequencies satisfying the request of wideband constant beamwidth can be deduced from the one at the reference frequency.
将特征向量等效为自适应滤波器的权向量，通过合理选择该滤波器期望响应递推求解出特征向量。
On the basis of the equivalence of weight vector of an adaptive filter and the eigenvector, choosing a reasonable filter expectation response, the eigenvector was estimated.
证明判断矩阵一致性的一个充要条件，并根据此充要条件，提出一种直接进行排序权向量计算的方。
On the basis of the condition, this thesis puts forward a direct calculation method of weight vector in ranking.
关于算法分析的定理证明了这种混合算法对于紧致集内的权向量构成的任意连续函数能依概率1收敛于全局极小值。
It is shown that this algorithm ensures convergence to a global minimum with probability 1in a compact region of a weight vector space.
首次提出并建立了用于产品设计方案评价的专家权向量、目标向量、约束向量、方案评价的目标矩阵和约束矩阵。
A system of evaluation criteria, namely the expert authority vector, object vector, constraint vector, object matrix and constraint matrix is put forward for project appraisal in conceptual design.
介绍了RBF网络线性层权值的训练算法——递推最小二乘法，及中心向量的动态递推算法。
A recursive least squares algorithm for linking weight between linear layers of RBF network is introduced, and a dynamic recursive algorithm of center vector is proposed.
设计了网络的运行机制和网络权值向量的学习机制。
How to run the network and how to adjust the weights of the network were designed.
该方法首先在不知道任何基阵方向向量先验知识的情况下，利用信号的多普勒信息估计波束形成的权矢量。
The weight vector of beamforming is estimated by Doppler information of the signal first, then it is approximated by RBFNN to carry out the blind optimizing beamforming.
在第一种改进方案中，各单元的权值向量根据各单元和BMU之间对应各坐标的差进行更新。
In the first improved scheme, the weight vectors of the units were updated according to the differences of the corresponding coordinates between the units and the BMU.
对不同频带的平稳分量建立相应的最小二乘支持向量机预测模型，将各模型的预测值等权求和得到最终预测值。
The different least square support vector machine (LS-SVM) models to forecast each IMF are built up. These forecasting results of each IMF are combined to obtain the final forecasting result.
卡尔曼滤波方法已广泛应用于动态测量数据的处理，其单位权方差通常按新息向量和观测值的残差向量进行计算，增加了观测值残差向量的计算。
Kalman filtering method is widely used in data adjustment of dynamic surveying system, whose unit variance is usually computed with innovation vector of parameters and residual vector of observations.
设计了一种模糊评价算法来计算权值向量。
A fuzzy evaluation algorithm is proposed for calculating index weight values.
灰色评价权矩阵(6)根据确定出的指标权重向量和灰色评价权矩阵，得到综合评价矩阵。
Grey evaluation weight matrix (6) according to identify the index weight vectors and grey evaluation weight matrix get comprehensive evaluation matrix.
然后采用运动向量归一化、噪声向量滤除、权值扩展向量中值（WEVM）滤波及前帧分割结果后向投影技术来得到对象的运动掩码；
Then the methods of normalizing MV, weighted extended vector median (WEVM) filter on the MV fields and the existent segmentation results projection were utilized to obtain the moving mask of object.
PSO的位置向量对应模糊神经网络的权值向量，而PSO的适应函数对应模糊神经网络的目标函数，然后，通过演化PSO达到训练模糊神经网络的目的。
Position vector of a PSO is wight vector of trained FNN, and fitness function of the PSO is object function of trained FNN, the FNN is then trained by evolving the PSO.
自组织特征映射（SOFM）网络利用神经元权值向量表示输入数据的结构、具有较好的分类能力。
The self-organizing feature map (SOFM) uses weight of network to present structure of the input data and has preferable ability of classification.
我们把x_image和权值向量进行卷积，加上偏置项，然后应用ReLU激活函数，最后进行max pooling。
We then convolve x_image with the weight tensor, add the bias, apply the ReLU function, and finally max pool.
我们把x_image和权值向量进行卷积，加上偏置项，然后应用ReLU激活函数，最后进行max pooling。
We then convolve x_image with the weight tensor, add the bias, apply the ReLU function, and finally max pool.
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