Chinese translation for "train error"

方向误差

Example Sentences:

	1.	The choice of the value of the training error of a neural network is a challenging problem 神经网络训练误差值的选取是一个具有挑战性的问题。
	2.	The algorithm can keep the structure difference among networks meanwhile reducing the train error of the network 通过保持网络间的结构差异,提高神经网络集成预测的泛化能力。
	3.	With application to civil engineering , this thesis advises a new method for determining the training error of a neural network by taking into account the stochastic characteristic of the training samples 结合土木工程应用,考虑训练样本的随机特性,本文提出了一个新的选取训练误差初始值的方法。
	4.	This paper ' s study was emphasized on : 1 . set up bp neural network set up bp network by matlab toolbox . chosen transfer function among every layer , training error and learning rate were included 重点研究内容： ( 1 )建立bp ( backpropagation )神经网络用matlab提供的人工神经网络工具箱建立网络，包括选择各层间的传递函数、确定网络训练误差和学习速率等，为建立定价模型做准备。
	5.	As for a certain value of the training error , if the predicted value of a neural network model lies in true value confident interval , it is believed that the true value of a neural network output variable be obtained , and the training process of a neural network is over 该方法由实测值求出相应的真值置信区间,若训练误差的取值使得网络预测值落在真值置信区间内就可以认为此时的训练学习反映了网络输出变量的真值情况,学习就可以结束。
	6.	The main conclusions are as follows : through the different structure and algorithm application of bp model in the predication of regional groundwater hydrology , the hidden layers number , learning rates , neuron number of hidden layer and training errors of bp model and accelerated bp algorithm which influence the convergence effects and test results of model are compared each other . some application technology related parameters of bp structure design are put forward 论文取得了以下主要成果：通过不同bp网络结构和算法在区域地下水文预测中的实例研究，重点比较了不同层次结构、隐层单元数、学习速率、训练收敛误差等4个基本要素及不同算法、不同样本容量等对模型收敛效果、模拟、检验与预报结果的具体影响。
	7.	Based on the formers , this dissertation efficiently selects the face features abstracting using ica . with no decline of recognition rate , the feature dimension is reduced , so the course of recognition is accelerated . support vector machine pattern recognition method is based on vc dimension theory , adopting the srm principle and considering training error and the generalization ability , which has shown many special advantages in dealing with small samples , non - linear and pattern recognition in high dimension 本文采用基于矩阵s的人脸表示方法，将ica特征选择的概念和算法用于人脸特征的提取和优化，在不影响识别率的情况下，降低了特征维数，提高了识别速度；支持向量机( svm )模式识别方法基于vc维理论，采用结构风险化原理，兼顾训练误差和泛化能力，在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势；对于多类问题，介绍并采用了“一对一”的策略进行svm分类器设计；对于图像预处理，详细介绍了几何归一化的算法步骤。
	8.	1 . this thesis puts forward a new model ? the model of crop ' s water rising and vaporizing regression nerve networks ( nn ) . this model adds a deviation cell based on existing bp ( nn ) the effect of the deviation cell is to amend training errors of the nn , to increase convergence speed of the nn , and to reach expectant objective 本文主要完成了以下几个方面的内容： 1 、提出了一个新型的模型? ?作物蒸腾蒸发回归神经网络决策模型，该模型以已有的bp神经网络为基础，增加了一个偏差单元，该偏差单元的作用是修正网络的训练误差、提高网络的收敛速度，从而达到预期的目的。
	9.	The result of fault diagnosis simulation tests indicates that the fault diagnosis system could make training error reach to the aim value quickly and efficaciously for all pulverizing system fault samples . at the same time , simulation tests prove that the fault samples with the signal of zero or one of pulverizing system and bp neural network model are correct , and this system faults can be diagnosed exactly and quickly . obviously , this research is successful and lay the foundation for the development of pulverizing fault diagnostic system 其故障诊断仿真实验结果表明，应用本文所开发研究的制粉系统各故障样本及其相关故障样本训练时均能快速有效地收敛于一个设定的系统误差值；同时其故障诊断的仿真实验证明了本文所建立的以0 、 1为征兆量的制粉系统故障样本和bp神经网络模型是正确的，且能快速、较准确地对故障情况作出判断，显然，本文的工作是有成效的，为制粉系统故障系统进一步开发奠定了基础。

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