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1)  Momentum Backpropagation Neural Network
动量反向传播神经网络
2)  BP neural network
反向传播神经网络
1.
The paper constructed a BP neural network on the basis of supervised learning,and built a model of extraction of gas disaster information by training the BP neural network.
文章基于有指导学习构造了反向传播神经网络,通过对该神经网络的训练建立了瓦斯灾害信息提取模型,并利用数据挖掘软件iDA对所建模型进行了分析,以发现和提取有价值或潜在的信息,从而达到了预防瓦斯灾害发生的目的。
2.
To circumvent the transmission performance degradation of the orthogonal frequency division multiplexing(OFDM) systems due to the nonlinear high power amplifiers(HPA),a new predistorter is presented which consists of two similar single-input and single-output BP neural networks(NN) in series.
针对非线性高功率放大器导致正交频分多址系统传输性能下降问题,采用两个类似结构的单输入单输出反向传播神经网络串联后级联高功率放大器实现其预失真。
3)  back-propagation neural network
反向传播神经网络
1.
The composite close-loop intelligent PID control algorithm based on back-propagation neural network is presented in this paper.
在单轴气浮转台控制算法研究上,针对单轴气浮台测控系统的高精度和低速度要求,常规PID控制在此转台控制中无法达到满意的控制效果,本文提出了一种基于反向传播神经网络复合闭环的智能PID控制算法。
2.
Back-propagation neural network(BP) and Support Vector Machine(SVM) models can improve identification rates of 31 P MRS to 92.
结果有限的样本实现了良好的分类性能,反向传播神经网络(BP)和支持向量机(SVM)模型可以提高31P MRS识别率,识别率可达92。
3.
This paper analyzed the basic theory and algorithm of the probabilistic neural network,and established certain equipment fault classification model based on the PNN and improved BPNN,simulation showed that PNN model outperforms the improved back-propagation neural network model in classification speed,precision and generalization ability.
首先分析了概率神经网络(PNN)的基本结构及其训练算法,建立了某型航空发动机故障分类的概率神经网络模型,通过对该设备故障进行定性诊断,对比分析了概率神经网络与常用的误差反向传播神经网络(BPNN)分类模型对各类故障的分类效果。
4)  Artificial neural net
反向传播-人工神经网络
5)  back propagation neural network
反向传播神经网络
1.
A prediction method based on genetic algorithm(GA) and back propagation neural network(BPNN) was proposed to solve springback problem of sheet metal after air-bending forming and punch design.
为解决金属板材折弯成形的回弹及凸模设计问题,提出了一种基于遗传算法的反向传播神经网络预测方法。
6)  baek-propagetion network
前馈反向传播神经网络
补充资料:反向传播学习
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性质: 又称BP算法。人工神经网络中一种审订式的、误差校正式的学习。在输出层计算出一个误差后,通过网络反向传播测定贡献给输出误差的每个连接权值大小,基于每个连接权值对误差的贡献来调整权值,使得总的输出误差为最小。

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