The study of the rough set neural networks based on SOFM

Objective: This paper refers to a New Rough Set neural network based on SOFM. The model perfectly solves many problems, such as the effects of training sample size and sample quality on accuracy of artificial neural network. Besides, the new network has reduced computation and time training needed, simplified the neural network structure and improved the system speed. Method: Combining the rough set with the neural network which bases on self-organized feature map (SOFM), it has presented an architecture of rough set neural network system in this paper. The paper also designs a system flow chart and describes work principle of each part. Result: The validity of these models has been tested by practical examples. Experimental results indicate that the system not only increases the quality and rate of diagnosis, but also reduces the measure items and diagnosis costs, which makes the result visualized and it has favorable applied prospect. Conclusion: The calculation result of New Rough Set neural network based on SOFM is reliable. The new model synthesizes the advantages of rough set theory and neural network.