利用水质遥感监测信息和地面监测信息的互补性，提出一种基于神经网络一证据理论的数据融合处理方法。
Using complementarity of remote sensing information and ground monitoring information on water quality, a data fusion method based on neural network and evidence theory is proposed.
把影像的空间信息融入分类决策，提出了一种基于证据理论与神经网络的遥感影像分类方法。
The spatial information of the image and evidence theory is applied to classification of remote sensing image based on neural networks.
利用系统的冗余信息，提出了基于D - S证据理论对集成神经网络的输出进行融合的方法。
To take advantage of redundancy information, this paper gives a new method that implement the output data fusion of integrated neural network based on the D-S evidence theory.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
本文主要涉及的不确定推理模型包括主观贝叶斯的概率推理模型，可信度理论推理模型，证据理论及其改进推理模型以及神经网络推理模型。
In the paper, the models of uncertain reasoning are focused, such as the reasoning model of Bayes probability, Reliability theory, D-S evidence theory and Neural Network.
提出一种基于RBF神经网络和D - S证据理论相结合的数据融合结构应用于轴承故障诊断。
This paper gives a data fusion structure based on RBF neural network and D-S inference and its application in the fault diagnosis of bearing.
该推理模型前级采用神经网络并行子网，用于目标的预分类，后级采用证据理论用于多周期的不确定性推理和概率的全局分配。
The forestage of the fusion model completes target presort and its post-stage is used to multi-period uncertainty inference and the whole set distribution of probability.
该推理模型前级采用神经网络并行子网，用于目标的预分类，后级采用证据理论用于多周期的不确定性推理和概率的全局分配。
The forestage of the fusion model completes target presort and its post-stage is used to multi-period uncertainty inference and the whole set distribution of probability.
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