本文提出了一种基于主分量分析法和反向传播神经网络的图像识别方法，并详细阐述了这种方法的具体实现过程。
Dynamic and static mapping techniques are introduced here as the qualitative methods while statistical methods, inverse calculation and principle component analysis are introduced a.
反向传播人工神经网络对正常肝和脂肪肝的识别率均为100%。
The accuracy rate of neural network was 100% both for normal liver and fatty liver.
以人工神经网络的前馈型网络为基础结构，基于反向传播算法进行学习和训练来拟和证券价格指数的运动趋势。
In this model, back propagation algorithm based on forward networks was conducted to learn information of historical data and to train the network weights.
方法：联合应用反向传播人工神经网络和吸光度减法技术。
Method: The artificial neural network of reverse transmission and absorbance subtraction technique were used.
通过找出其隐式函数关系，误差反向传播神经网络可以实现输入和输出间的任意映射。
With the implicit function relation, BP (Back Propagation) neural network can easily realize the mapping between input data and output data.
为了提高从软件构件库中搜索构件的速度和准确率，利用误差反向传播神经网络(BP网络)研究了构件的搜索问题。
To improve the speed and accuracy in component retrieval from software component database, BP network was introduced in the research of component searching.
应用神经网络的误差反向传播算法(BP)和大量的实测数据样本训练出了能在线诊断四种加工状态的BP模型并成功地诊断了实际加工状态。
The BP algorithm of Artificial Neural Networks and lots of experimental samples were used in training the BP model which succeeded in diagnosing four kinds of operational status.
探讨了利用反向传播神经网络和BP算法确定市场响应函数的方法。
This paper discusses backpropagation neural network model and BP algorithm to determine market response functions.
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.
比较而言，学习矢量量化网络和概率神经网络在分类能力方面要比反向传播网络好一些，概率神经网络在计算负载方面比学习矢量量化网络要更胜一筹。
By comparison, LVQ network and PNN network are better than BPN network in classification ability, and PNN network is better than the others in computation load.
使用自适应谐振理论（ART）和误差反向传播（B）两种神经网络，开发了汽轮发电机组振动故障诊断模型。
The vibrating fault diagnosis system for turbo-generator unit based on ART and BP network is developed in this work.
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.
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.
应用推荐