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1·In this paper, a new scheme to realize O-QAM system by means of cascading a discrete cosine transformer (DCT) and a weighting network is presented.

本文提出用离散余弦变换(DCT)处理器级联一个权重网络实现O - QAM系统的新方案。

2·In this paper, we mainly study on properties of digital beamforming with subarray, construction of weighting network and an analog and digital weighting approach with plane array.

本文主要研究了子阵级数字波束形成的性能、加权网络的构建以及一种平面阵的模拟和数字加权方法。

3·In particular, I will introduce a local search algorithm for proximity query, a node weighting method for local clustering, and the network of networks model for integrating multiple networks.

特别的，我还将介绍本地搜索算法的接近查询，和一个局部聚类的节点加权方法，以及网络的网络模型集成多个网络。

4·Compared obtained value with corresponding expected value, error was sent to network with reverse learning algorithm, so that renovate connect weighting value.

获得的值与对应的期望值对比，误差使用反传学习算法传回网络，用以更新连接权值。

5·Through design of the statistical weighting algorithm based on BP neural network, the data fusion model will be established.

通过设计基于BP神经网络的统计加权算法，建立数据融合模型。

6·This paper presents a basic principle of making network processing, following the trail of isoline, and smoothing curves by near-point average weighting method.

本文介绍了用近点平均加权法进行网格化处理、追踪等值线以及曲线的光滑等基本原理。

7·The intervals of main roof weighting at work faces were predicted by using artificial neural network.

利用人工神经元网络对工作面的顶板来压步距进行了预测。