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Fuzzy Graphs Clustering with Quality Relation Functionals in Cognitive Models

机译:模糊图与认知模型中的质量关系功能聚类

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In this study we present a new approach for developing input-output data set (antecedents, consequents) for fuzzy rules of expert system production based on the mechanism of fuzzy logic inference. Integration of the methods for cognitive modeling and of analysis with the expert system has been proposed. To generate the logical conclusion attributes on the basis of fuzzy graph models, the clustering procedure and the detection of system response are used. The approach, called Self-Constructing Attribute Generator, SCAG, consists in consecutive transformation of the initial matrix of the fuzzy graph model using two types of quality functionals. The first step is the initialization of the primary transformation matrix to upper-triangular sight using the square barrier penalty functions and "inverse" functions. At the second stage, the feedback on disturbances is generated in the form of a vector set. Further graph clustering is directly made based on the minimization of the potential energy functional.
机译:在这项研究中,我们提出了一种新的方法,可以基于模糊逻辑推断的机制开发用于开发专家系统生产的模糊规则的投入输出数据集(前所未知,后果)。提出了认知建模和与专家系统分析方法的集成。要在模糊图模型的基础上生成逻辑结论属性,使用聚类程序和系统响应的检测。该方法称为自建属性生成器SCAG,包括使用两种质量函数的模糊图模型的初始矩阵连续转换。第一步是使用方形屏障惩罚功能和“逆”函数初始转换矩阵到上三角视线。在第二阶段,以向量集的形式产生干扰的反馈。进一步的图形聚类是基于最小化势能功能的。

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