Rough neural network modeling based on fuzzy rough model and its application to texture classification
On the basis of fuzzy rough model (FRM), a method to construct rough neural network FRM_RNN_M is proposed. By means of adaptive GaustafasonKessel (GK) clustering algorithm, fuzzy partition can be accomplished in input feature space. Then based on the search of cluster numbers and attribute subsets, optimal FRM model will be found, and by integrating it with neural network technique, FRM_RNN_M is constructed. The experiment results of classifying Brodatz texture image indicate that FRM_RNN_M is superior to traditional Bayesian and learning vector quantization (LVQ) methods, moreover, FRM_RNN_M has more powerful comprehensive soft decision-making ability than single FRM model. Also, experimental results show that it is favorable to obtain better FRM model if the search of reductive attribute subsets is included. Compared with conventional rough logic neural network (RLNN), FRM_RNN_M has superiorities in the size of structure, convergence speed and generalization ability.
rough neural network texture classification rough sets rough data model data analysis