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Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

机译：基于自底向上模糊粗糙数据分析的粗糙神经网络

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摘要

Based on bottom-up fuzzy rough data analysis, a new rough neural network decision-making model is proposed. Through supervised Gaustafason-Kessel (G-K) clustering algorithm, proper fuzzy clusters are found to partition the input data space. At the same time cluster number is searched by monotone increasing process. If the cluster number matches with that exactly exist in data sets then excellent fuzzy rough data modeling (FRDM) model can be built. And by integrating it with neural network technique, corresponding rough neural network is constructed. Our method overcomes the defects of conventional top-down based rough logic neural network (RLNN) method, and it also achieves adaptive learning ability and comprehensive soft decision-making ability compared with FRDM model. The experiment results indicate that our method has stronger generalization ability and more compact network structure than conventional RLNN.
机译：基于自下而上的模糊粗糙数据分析，提出了一种新的粗糙神经网络决策模型。通过监督的Gaustafason-Kessel（G-K）聚类算法，找到合适的模糊聚类来划分输入数据空间。同时，通过单调递增过程搜索簇数。如果集群编号与数据集中确切存在的集群编号匹配，则可以建立出色的模糊粗糙数据建模（FRDM）模型。并通过与神经网络技术的集成，构建了相应的粗糙神经网络。该方法克服了传统的基于自顶向下的粗糙逻辑神经网络方法的缺陷，与FRDM模型相比，还具有自适应学习能力和综合的软决策能力。实验结果表明，与常规RLNN相比，该方法具有更强的泛化能力和更紧凑的网络结构。

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