一种基于模糊划分的粗糙神经网络

Xulong Xiang, Zhang Dongbo, Wang Yaonanr, Liu ZiWen
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引用次数: 0

摘要

针对粗糙神经网络构造过程中的划分问题,提出了一种基于模糊聚类的话语域划分方法。针对传统聚类算法容易陷入局部最优的问题,将一种带有交叉和变异算子的改进粒子群算法与FCM算法相结合。提出了一种新的模糊聚类算法(CMPSO-FCM)。该算法提高了搜索能力和聚类效率。然后利用模糊划分矩阵计算用于属性约简的模糊相似矩阵,并在得到模糊聚类结果后定义模糊相似测度;利用熵值法获得一组模糊粗糙决策规则。最后,根据这些决策规则设计了一个粗糙神经网络。实验结果表明,与传统的粗糙神经网络相比,该方法在结构、分类精度和泛化方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel rough neural network based on fuzzy partition
A method to partition the universe of discourse based on fuzzy clustering is proposed to solve the partition problem in the process of constructing rough neural network. Considering traditional clustering algorithm has the problem of easily fall into local optimum, a modified PSO algorithm with crossover and mutation operators is combined with FCM algorithm. And a new fuzzy clustering algorithm (CMPSO-FCM) is proposed. The searching capability and clustering effectiveness are improved by this new algorithm. Then the fuzzy similar matrix, which is used for attribute reduction, is calculated by using fuzzy partition matrix and the definition of fuzzy similar measure after fuzzy clustering result is achieved. And a set of fuzzy rough decision rules are acquired by entropy method. Finally, a rough neural network is designed under these decision rules. Experiments results show that, compared with traditional rough neural network, this method has superiorities at the aspect of structure, classification precision and generalization.
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