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 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.
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