通过输入网络为基础的数据来建立一个使用者情绪和偏好的实时测量工具并不是新点子。
The idea of tapping web-based data to build a real-time measure of users' emotions and preferences is not new.
在成本分析和参考工时技能参数法的基础上，选取了10个成本影响因素作为神经网络的输入。
Based on cost analysis and the reference of working hour technique parameters, 10 factors affecting the cost are selected as the input of the Neural Network.
在此基础上，采用主成分分析法对“五因素”进行特征提取，降低BP网络的输入维度。
On the above basis, we used principal component analysis of the "five factors" for feature extraction and reduced the input dimension of BP network importation.
为了实现网络服务功能匹配，在扩展和补充已有相关研究的基础上，提出了基于明确界定输入与输出的网络服务功能匹配方法。
In order to realize function matching of Web services, an input and output-based function matching method was proposed based on extension and supplements of existing studies.
以MATLAB6.5BP神经网络工具箱为基础，编写了基于频率平方变化和振型模态分量为输入参数的BP神经网络结构损伤识别程序。
Using MATLAB6.5 BP neural network toolbox, compiles the structural damage detection process with the input parameters based on the modal parameters of frequency and mode shape.
本文在直积网络概念的基础上，提出了一种多输入多输出直积系统的结构。
The mathematical model and structure for a multi-input-multi-output direct product system are proposed based on the direct product concept of group theory.
并对该特征向量进行对数归一化，将归一化的特征向量作为径向基函数（RBF）神经网络的输入，在此基础上进行识别，达到较好的识别效果。
The normalized vector is used as the input of RBF NN, and target recognition is performed based on this, which leads to a satisfactory recognition result.
在神经网络训练的基础上，采用遗传算法优化神经网络的输入参数。
Based the successfully trained ANN model, genetic algorithms (GA) are used to optimize the input parameters of the model.
针对模糊神经网络不能接受离散标称变量输入的缺陷，在CCT模糊神经网络和模糊聚类方法的基础上，提出了一种混合模糊神经网络建模方法。
A Hybrid fuzzy neural network modeling method was presented. On the basis of CCT fuzzy neural network and fuzzy cluster method, this method can deal with discrete variables input.
针对模糊神经网络不能接受离散标称变量输入的缺陷，在CCT模糊神经网络和模糊聚类方法的基础上，提出了一种混合模糊神经网络建模方法。
A Hybrid fuzzy neural network modeling method was presented. On the basis of CCT fuzzy neural network and fuzzy cluster method, this method can deal with discrete variables input.
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