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规则提取方法    
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  rule extraction approach
    Research on Fault Diagnosis Rule Extraction Approach Based on Genetic Algorithm
    基于遗传算法的故障诊断规则提取方法的研究
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Abstract This paper discusses and compares the features of fuzzy inference systems and Artificial Neural Networks(ANN), and presents a fuzzy inference model based on neural networks. In this model, neural network, with their superb learning capabilities,extract fuzzy rules from samples. So, the fuzzy system presented here can formulate fuzzy rules adaptively according to various samples. The model's framework, ANN's architecture,and fuzzy inference methods are introduced in this paper.

通过分析模糊理论与人工神经网络在知识处理方面的特点,将人工神经网络技术结合到模糊推理系统中,以模糊推理为核心,采用人工神经网络技术提取模糊规则,形成一类具有学习功能的模糊智能系统。本文介绍了该模型的基本思想,规则提取方法及模糊推理方法。

In this paper, the rule extraction of neural networks is disscussed, which is an effective method to avoid the shortcoming of being “black boxes”. Techniques based on decompositional and input output mapping approaches are studied and their fundamental concepts and evaluates their performances are generalized. Based on similar weight approach, the CSW approach is proposed to efficiently solve the rule extraction from continuous\|input neural networks. CSW is applied in IRIS Flower Classification Problem,and...

In this paper, the rule extraction of neural networks is disscussed, which is an effective method to avoid the shortcoming of being “black boxes”. Techniques based on decompositional and input output mapping approaches are studied and their fundamental concepts and evaluates their performances are generalized. Based on similar weight approach, the CSW approach is proposed to efficiently solve the rule extraction from continuous\|input neural networks. CSW is applied in IRIS Flower Classification Problem,and experiment results show that rules extracted by our method are accurate and comprehensible.

文中论述了作为解决神经网络“黑箱问题”有效手段的规则提取方法,分析了基于结构分解和输入输出映射的神经网络规则提取的各种算法,概括了它们的基本思想并分析了它们的优劣,在相似权值法的基础上提出 C S W 算法,有效解决了连续值输入网络的规则提取问题.将 C S W 算法应用于 I R I S分类问题取得了良好的效果

A new method for extracting the rules of the fuzzy associative memory (FAM) system is presented. Based on different prior knowledge, the problem of generating the control rules can be transformed into the problems of clustering the planes and partitioning the feature space, both of which have mature solution algorithms. Finally the effectiveness of the method is checked with an example of the truck backer upper control system.

基于模糊联想记忆(FAM)系统的特点,阐述了一种FAM系统规则提取的方法.在已知控制系统输入特征空间的划分时,规则的提取可以转化为聚类平面的问题;当可以确定输出变量的语言值的个数时,首先利用数据各维直方图抽取平衡点处的规则,然后通过聚类平面将其余规则的提取转化为分类器特征空间的划分问题,用树型分类器确定输入特征空间的划分.倒车系统FAM控制器的设计证明了该方法的有效性.

 
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