基于论域空间模糊划分的粗集神经网络

针对粗集神经网络构建过程中的论域空间划分问题，提出一种基于模糊聚类的论域划分方法。将带交叉变异算子的粒子群优化算法(PSO)与模糊C-均值聚类算法(FCM)相结合，给出一种新的模糊聚类算法CMPSO-FCM，该算法具有良好的搜索能力和聚类效果。提出一种基于信息熵的模糊粗糙集决策规则获取方法，并用获取的规则指导粗集神经网络的构建。实验结果表明，该方法构造的神经网络具有更精简的结构、较好的分类精度和泛化能力。

Rough Set Neural Network Based on Fuzzy Partition in Universal Space

Aiming at the problem of the universal space partition in the process of constructing rough set neural network, this paper proposes an universe of discourse method based on fuzzy clustering. 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 the new algorithm. A set of fuzzy rough decision rules are acquired by entropy method, and a rough set neural network is designed under these decision rules. Experimental results show that this method has superiorities at the aspect of structure, classification precision and generalization.