Title :
A novel rough neural network based on fuzzy partition
Author :
Xiang, Xu ; Dongbo, Zhang ; Yaonanr, Wang ; Ziwen, Liu
Author_Institution :
Coll. of Inf. Eng., Xiangtan Univ., Xiangtan, China
Abstract :
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.
Keywords :
entropy; fuzzy set theory; matrix algebra; neural nets; particle swarm optimisation; pattern clustering; rough set theory; CMPSO-FCM; FCM algorithm; PSO algorithm; attribute reduction; crossover operator; entropy method; fuzzy clustering algorithm; fuzzy partition matrix; fuzzy rough decision rules; fuzzy similar matrix; fuzzy similar measure; mutation operator; partition problem; rough neural network; Clustering algorithms; Educational institutions; Electronic mail; Entropy; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Genetic mutations; Neural networks; Partitioning algorithms; FCM algorithm; PSO algorithm; attribute reduction; entropy; fuzzy clustering; rough neural network;
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
DOI :
10.1109/CCDC.2010.5498822