To solve the contradiction problem, a lightweight, energy efficient, in-network lossless aggregation and hierarchical cluster was proposed based on data collecting mechanism (QTBDC).
为解决上述矛盾问题，提出了一种轻量级的、能量有效的、基于无损聚合的层次分簇数据收集机制(QTBDC)。
First, the new scheme of a lossless data compression based on neural network is given by using non-statistical method.
本文首先采用非统计的方法给出了基于神经网络的无损数据压缩新方案。
A lossless data compression scheme based on neural network is obtained through the structure specific mapping, integral function and BP neural network.
通过构造特别的映射、整函数和BP神经网络，获得一套基于神经网络的无损数据压缩方案。
In this paper, a lossless compression method, based on adaptive prediction, is presented. This method USES neural network model to modify the prediction weight.
本文提出一种基于自适应预测的无损压缩方法，该方法利用神经网络模型自学习的能力，自适应的调整预测器的预测系数。
A statistical regressive mathematical model for processing the measured data of lossless network is presented.
提出了处理无耗正切关系网络测量数据的统计回归数学模型。
Through lossless compression of geometric data in the network transmission, an efficient method which alleviates the network burden is proposed.
通过对网络传输中的几何数据进行无损压缩，提出了一种能有效缓解网络负荷的方法。
A study has been made of the power divider which is a lossless even symmetric and reciprocal network.
本文研究的一分为二功率分配元件是无耗偶对称互易网络。
This paper introduces the ideal transformer equivalence and Tangent parameter characteristic of lossless reprocical two-port network.
本文从微波网络的S参数出发，导出表征无耗互易二端口网络的正切参数。
This paper introduces the ideal transformer equivalence and Tangent parameter characteristic of lossless reprocical two-port network.
本文从微波网络的S参数出发，导出表征无耗互易二端口网络的正切参数。
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