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将粗糙集从原始数据中提取数据的能力和模糊神经网络的推理能力有效地集成起来。使用增量式规则提取算法(IREA)从原始数据中抽取构建模糊神经网络(FNN)所需的规则集。与传统的模糊神经网络相比较,使用IREA算法构建的FNN具有较短的规则长度和更少的规则条数。网络拥塞仿真试验验证了本文所述方法的优越性。
The ability of rough set to extract data from the original data and fuzzy neural network reasoning ability are effectively integrated. The incremental rules extraction algorithm (IREA) is used to extract the rule sets needed to construct a fuzzy neural network (FNN) from the raw data. Compared with the traditional fuzzy neural network, the FNN constructed by using IREA algorithm has a shorter rule length and a smaller number of rules. Network congestion simulation tests verify the superiority of the method described in this article.