Research on Transformer Fault Diagnosis with Rough Sets Theory and BP Neural Networks
Gong Hui · Qingdao Haiyang Daxue xuebao · 2005
In this article,aiming at transformer fault diagnosis,a diagnosis model based on rough set theory and BP neural networks is brought forward and the realization steps of the model are analyzed.After the continuous attributes are discretized with a Kohonen neural network,rough sets theory is used to simplify the attribute parameters.The reduction results are transformed into rules,which are used as input of the BP neural network.The simulations show that the learning speed and diagnosis correctness are greatly improved after the training data is processed by rough sets,and the computation time is decreased by using rough set theory.