Classifier Based on Rough Set and Neural Network and its'''' Application in LPR
基于粗糙集和神经网络的分类器及其在LPR中的应用

In pattern regconition, both rough set theory and neural network can be used to classify patterns. The two theory don't have much common point, but they can be complementary when we combine them in designing a classifier and the recognition effect can be better. This article gives the max output error when the rough set is used to reduce a nerve cell or a connection in Back-Propogation(BP)neural network. Then, rough set theory and BP network are inte- grated into one classifier. we validate this classifier's validity by using it in the character recognition of licence plate. The experiment shows that this classifier is better than classifiers using rough set or BP neural network in recog- nition rate and recognition speed.