基于模糊神经网络的多传感器数据融合方法

Y. Ling, Xiaoguang Xu, L. Shen, Jingmeng Liu
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引用次数: 12

摘要

利用模糊神经网络融合多传感器数据的不确定性,将传感器的测量数据作为模糊神经网络的输入,然后进行模糊处理。然后利用神经网络规则对数据进行分析和处理。最后是去模糊后的输出。面对隶属函数不确定的输入模糊化问题,采用黄金分割法确定模糊化层隶属函数的初始中心和宽度。将模型模糊化的方法和改进的BP网络学习规则引入到网络判断规则中,根据权值规则去模糊化后输出判断结果。本文给出了一种基于模糊神经网络的多传感器数据获取的通用方法。网络结构合理,具有较快的训练速度。它还具有良好的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi sensor data fusion method based on fuzzy neural network
With the uncertainty of the multi sensor data of the fuzzy neural network fusion, the measure data from sensors is used to as the input of the fuzzy neural network and then is fuzzed. Next the data is analyzed and disposed by the neural network rule. Finally it is output after defuzzification. Confronting with the input fuzzification with uncertain membership function, we adopt the golden partition method to decide the initial center and width of membership functions of the fuzzification layer. The way of the model fuzzification and the improved BP network study rule is introduced to the network judging rule, and the judging result is output after defuzzification according to the weight rule. The article gives a general method of the multi sensor data gaining based on fuzzy neural network. The structure of network is rational and has rather quick training speed. It also has good generalization ability.
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