证据理论和模糊积分融合方法可以减少决策过程中的不确定性，大大的提高了决策的精度。
The evidence theory and fuzzy integral can abate the incertitude and improve the accuracy of decision.
模糊积分是一种有效的决策层融合目标识别方法。
Fuzzy integral is an effective decision level fusion method for target recognition.
针对可见光和红外热图像序列中的远距离目标检测问题，提出了一种基于模糊积分的可信度融合的目标检测方法。
Aimming at long distance targets detection problem in visual and thermal infrared image sequences, a confidence fusion detection method based on fuzzy integral.
最后，用模糊积分将子空间分类结果融合，得出最终类。
In the end, classification results of each subspace are fused by means of fuzzy integral to get the final class.
为了进一步提高网络入侵检测系统的检测性能，将模糊积分理论和神经网络技术应用到网络入侵检测中，提出了基于模糊积分的多神经网络融合模型MNNF。
The model of Multiple Neural Networks by Fuzzy(MNNF) integral presented in this paper is an effective method to improve the detection performance of network intrusion detection system.
该算法可利用模糊积分的单调性，通过计算各信息源的模糊度量值来融合结果集并且评价排序效果。
The result set is merged and the evaluation is ranked by using the monotonicity of fuzzy integral and calculating the fuzzy measure values.
对分布式信息检索的结果集采用模糊积分进行了融合，推导并给出了分布式信息检索的模糊积分算法。
A fuzzy integral algorithm of the distributed information retrieval is derived and given to merge the distributed information retrieval result set.
实验表明，采用新的基于模糊积分的融合方法，只要选择适当的模糊密度，就可以使得融合图像在提高空间细节质量的同时，相比其它融合算法能够具有更好的光谱质量。
The experiment shows that while the developed method can keep spectral quality of fused image, it can update the spatial detail quality of fused image when appropriate fuzzy density is chosen.
在无损检测信号处理和特征构造的基础上，用神经网络对缺陷进行识别，然后运用模糊积分对多个神经网络的分类结果进行融合。
In this paper, defects for NDT are classified with neural network which USES features extracted from signal processing. Then classification results from networks are fused with fuzzy integral.
最后，用模糊积分将子空间分类结果融合，得出最终类。试验表明本算法速度较快、精确度高。
Thirdly, standardization and dimension reduction are performed to classify signals in each signal subspace. In the end, classification …
最后，用模糊积分将子空间分类结果融合，得出最终类。试验表明本算法速度较快、精确度高。
Thirdly, standardization and dimension reduction are performed to classify signals in each signal subspace. In the end, classification …
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