Title :
An attribute recognition system based on rough set theory-fuzzy neural network and fuzzy expert system
Author :
Liu, Mei ; Quan, Taifan ; Luan, Shaohua
Author_Institution :
Dept. of Electron. & Commun. Eng., Harbin Inst. of Technol., China
Abstract :
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.
Keywords :
deductive databases; expert systems; fuzzy neural nets; knowledge based systems; pattern recognition; rough set theory; unsupervised learning; attribute recognition system; fault tolerance; fuzzy expert system; fuzzy neural network; knowledge database; knowledge rules; nonlinear mapping; rough set theory; self learning; Databases; Expert systems; Fault tolerant systems; Fuzzy neural networks; Fuzzy reasoning; Hybrid intelligent systems; Knowledge engineering; Modems; Neural networks; Set theory;
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
DOI :
10.1109/WCICA.2004.1342015
