它由基于反向信道预测的功率补偿算法和常规闭环功率控制组合而成。
It consists of a power compensation algorithm based on reverse channel prediction and a common closed -loop power control algorithm.
无论是那种市场方向，在一波趋势过后使用这些百分比可以预测反向趋势的摆动程度。
Apply these percentages after a trend in either direction to predict the extent of the countertrend swing.
该文介绍了一种基于人工神经网络的软件失效预测模型，给出了基于反向传播算法的多层前向网络的网络结构。
This paper presents a kind of software faults prediction model based on artificial neural network and the structure of the feed-forward multi-layer network with backpropagation learning algorithm.
选取正交设计的试验点作为反向传播人工神经网络的训练集，实现对全实验域试验点的预测，并与实测的试验数据比较。
The trials of orthogonal design were chosen as the training set of the BP-ANN, and forecasted the trails of the entire field.
将反向传播人工神经网络(BP - ANN)用于邻苯二甲酸酯类化合物的光化学降解的预测中。
Back propagating artificial neutral net (BP-ANN) was applied to the forecasting of photochemical degradation of phthalates.
本文将神经网络引入到航材需求分析领域中，应用误差反向传播网络建立模型进行预测，并对模型结果进行了分析。
This paper introduces Neural net to the fields of air-materials demands analysis, and applies Back Propagation network to forecast.
利用神经网络的误差反向传播算法(BP算法)，结合告警、天气和工程设计几方面的数据资料建立了微波中继段告警分析预测模型。
By means of BP (error back propagation) artificial nerve network, with data from alarm, weather and engineering documents, microwave hop performance analysis and forecast model is established.
在实际工业数据上进行的实验结果表明，支持向量机模型对丙酮纯度具有良好的预测效果，性能优于反向传播神经网络和径向基网络模型。
The experimental results on the real industrial data demonstrate that the model based on SVM achieves good performance and has less prediction errors than those of BPNN and RBFNN models.
本文首次将反向传播(BP)神经网络理论应用于13cNMR对1h nmr化学位移值的预测。
The Back Propagation (BP) neural network theory is first used to predict the relation between the data of 1h NMR and 13c NMR.
基于误差反向传播的机制，针对连续制造过程的预测与控制，提出多层神经网络的逐个样本学习算法。
A one-by-one learning algorithm for multi-layer neural network modelling is presented based on the back-propagation mechanism of network error.
基于误差反向传播的机制，针对连续制造过程的预测与控制，提出多层神经网络的逐个样本学习算法。
A one-by-one learning algorithm for multi-layer neural network modelling is presented based on the back-propagation mechanism of network error.
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