An attribute recognition system based on rough set theory-fuzzy neural network and fuzzy expert system

Since it is hard to get training set of fuzzy neural network, to understand knowledge rules, and to learn new knowledge through fuzzy expert system, an attribute recognition system based on rough set theory-fuzzy neural network and fuzzy expert system has been put forward. In this paper, it has explained how to use rough set theory to get training set of fuzzy neural network, how to deal with data through fuzzy neural network and fuzzy expert system parallelly, and how to acquire new knowledge from fuzzy neural network to supplement the knowledge database of fuzzy expert system. It has fully utilized the capability of rough set theory that is to simplify large amount of redundant data, the capabilities of fuzzy neural network that are self-learning, fault-tolerant and highly nonlinear mapping, and the capability of fuzzy expert system that is reasoning quality in knowledge. Experiments show the exactness and high-efficient quality of this recognition system, and it has gotten more than 96% correct recognition rate.