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模糊规则提取     
相关语句
  fuzzy rule extraction
     A New Method for Fuzzy Rule Extraction
     一种新的模糊规则提取方法
短句来源
     As the quality of rules is very crucial in a rule-based system, a new fuzzy rule extraction approach is proposed.
     然后讨论了一种新的模糊规则提取算法,用于提取合理的分类规则;
短句来源
     This method firstly introduces the fuzzy rule extraction technology which is suitable for classification, then presents a two-level determination classification algorithm based on fuzzy rules.
     首先介绍了一种新的模糊规则提取方法 ,然后基于所提取的模糊规则给出了一个采用二级判决的分类算法 ,并利用IRIS数据对此分类算法进行了仿真测试。
短句来源
  fuzzy rules extraction
     Fuzzy Rules Extraction Based on Soft Clustering and Kalman Filter Method
     基于软分类和卡尔曼滤波方法的模糊规则提取
短句来源
     This method firstly introduces the fuzzy rules extraction technology based o n fuzzy C-means algorithm,then designs a new classification algorithm with the fuzzy rules,finally uses a heuristics algorithm to simplify the fuzzy rules.
     该方法首先介绍了基于模糊C均值聚类的模糊规则提取,然后利用所建立的模糊规则库设计了一种分类算法,并且利用启发式搜索来精简分类规则。
短句来源
     AN APPROACH TO FUZZY RULES EXTRACTION OF NEURAL NETWORKS
     神经网络的模糊规则提取
短句来源
     The method of Automatic fuzzy rules extraction based on fuzzy BP net researches hidden key attributes through deleting redundant linking weight.
     建立基于模糊BP网络的自动模糊规则提取方法,它通过删除冗余连接权的方法寻找到网络的隐含关键特征。
短句来源
     Thus this algorithm establishes a new alternative to design fuzzy systems when the initial empirical knowledge is absent which usually leads to the difficulty in fuzzy rules extraction.
     因此,此模糊规则提取算法为解决模糊系统设计中由于经验不足,导致难以获取模糊规则的问题,提供了一个可行的方法。
短句来源
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  extracting fuzzy rules
     A Method of Extracting Fuzzy Rules Based on Neural Networks with Cluster Validity
     基于聚类有效性神经网络的模糊规则提取方法
短句来源
     A Method of Extracting Fuzzy Rules Based on Neural Networks with Cluster Validity
     基于聚类有效性神经网络的模糊规则提取方法
短句来源
     In this paper, a new method for extracting fuzzy rules from data is proposed based on radial basis function networks.
     提出了一种基于径向基函数神经网络 ( RBF网络 )的模糊规则提取的新方法。
短句来源
  “模糊规则提取”译为未确定词的双语例句
     This is the procedure of fuzzy-rule extraction.
     以上过程即完成了模糊规则提取过程。
短句来源
     A diagnosis method based on FMMNN (Fuzzy Min-Max Neural Network) was applied to the architecture to verify the advantages of the system.
     另外本文还将一种实验室开发的模糊最小最大神经网络(Fuzzy Min—Max Neural Network,FMMNN)的模糊规则提取方法应用于该系统以证实该分布式诊断系统的优越性.
短句来源
     Selecting Fuzzy Rules Using Clonal Algorithms and Its Application Study
     基于克隆算法的模糊规则提取及其应用研究
短句来源
     secondly,genetic algorithm optimizes choosing the fuzzy rule; finally,start the trigger to the sample that can't be discerned.
     首先给出一种模糊规则提取方法,然后遗传算法对模糊规则进行优化选择,最后对不能识别的样本启动触发器.
短句来源
     finally, start the trigger to the sample that can' t be discerned. This algorithm can improve the recognition capability of samples and reduce the rule sets. The performance of the classification algorithm is tested by the IRIS data, and the results show that not only it has very good classification performance but also the fuzzy rules sets is simplify.
     介绍一种基于模糊规则和遗传算法的分类算法.该算法分三个步骤:首先给出一种模糊规则提取方法,其次遗传算法对模糊规则进行优化选择,最后对不能识别的样本启动触发器.该分类算法可以在保证分类正确性高的前提下尽量减少规则数,并提高样本识别能力.本文最后用Iris数据对该分类系统进行仿真,研究表明该系统具有良好的分类能力和精简规则能力.
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  fuzzy rule extraction
However, as uncertainties in the data and missing values existed, a fuzzy rule extraction algorithm based on a fuzzy min-max neural network (FMMNN) was used.
      
As a second method, an existing fuzzy rule extraction method was extended to broken rotor bar detection problem.
      
  fuzzy rules extraction
It may be possible now to control the level of adaptation and forgetting through using different thresholds for fuzzy rules extraction.
      
  其他


In the generation of fuzzy systems,the primary work is to extract and modulate membership functions and fuzzy rules,but,using traditional methods, the amount of this work expands startlingly with the increasing of the number of variables.This paper presents a neural network based algorithm to automatically extract membership functions and rules of fuzzy systems.The complicated input-output relationship is firstly decomposed into the accumulation of simple input-output relationships.For each individual variable,the...

In the generation of fuzzy systems,the primary work is to extract and modulate membership functions and fuzzy rules,but,using traditional methods, the amount of this work expands startlingly with the increasing of the number of variables.This paper presents a neural network based algorithm to automatically extract membership functions and rules of fuzzy systems.The complicated input-output relationship is firstly decomposed into the accumulation of simple input-output relationships.For each individual variable,the algorithm will generate a set of membership functions that are appropriate for all simple input-output relationships.The fuzzy rules of the whole system are then generated based on these membership functions.At last,an example is given to show the potential of the method.

在模糊系统的生成过程中,最主要的任务是隶属函数和模糊规则的提取和调整,但用传统方法,其工作量往往随变量数的增长而爆炸性地增加.为了解决这一问题,本文提出了一种新颖的方法,利用神经网络来自动地提取模糊系统的隶属函数和规则.该方法首先将复杂的输入、输出关系分解成简单的输入、输出关系的叠加,然后对每个单独的变参产生一组适合于所有的简单的输入、输出关系的隶属函数,最后在这些变参的隶属函数的基础上求得整个系统的模糊规则。在本文的最后,我们给出了一个典型的实例以说明本方法的有效性。

The transformation of the extraction of fuzzy rules and fuzzy inference into the determination of the parameters of artificial neural network and neural computation is discussed and a fuzzy neural network (FNN) with learning function is presented. Further, the system steady-state error, integration saturation,etc are improved by using the trial and error method with dynamic factor tuning.

探讨了将模糊规则的提取和推理转化为人工神经网络参数的确定及神经计算,提出一种具有自学习功能的模糊神经网络(FNN),并用因子动态调整逼近法,解决了系统中静态误差和积分饱和等问题.

This paper discusses the technique for extracting of fuzzy rules. By converting the obtained fuzzy rules into a fuzzy neural network and learning repeatly, a concise rule base can be established together with the performance of fuzzy control be improved greatly.

本文讨论了一种模糊规则的提取技巧,再将所得的模糊规则转化成模糊神经网络(FNN),通过反复学习,得到精炼的规则库,从而提高模糊控制的性能。

 
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