Combining rough set theory with neural network theory for pattern recognition

Combination of kinds of artificial intelligence theories in application area of pattern recognition has become one of the most important ways of research of intelligent information processing. Neural network shows us its strong ability to solve complex problems for patter recognition. But neural network can´t tell the redundant information from huge amount of data, which will easily lead to some problems such as too complex network structure, long training time, low converging speed and much computation. Focusing on these problems, this paper proposes a method to combine rough set theory with neural network theory and uses it in pattern recognition. Experiments show the potential of this method.