In view of the inaccuracy and incompleteness of information systems, rough set reduction is implemented based on the improved discernibility matrix to delete irrelevant and unimportant data in the sample data, and then the sample set is trained and tested using neural network knowledge. A fault diagnosis system that combines rough sets with neural networks is designed. It is applied to TEP (Tennessee-Eastmanprocess) fault diagnosis and achieves good fault diagnosis results.
You Might Like
Recommended ContentMore
Open source project More
Popular Components
Searched by Users
Just Take a LookMore
Trending Downloads
Trending ArticlesMore