Rough sets theory and artificial neural networks applied in the transformer fault diagnosis
Xiaodong Yu · Relay · 2006
Rough set theory is a new intelligent information process technology.It can analyse and deduce all kinds of incomplete data,find the relationship between the data,pick up the useful characters and reduce the information process.Artificial neural networks has the essential nonlinear character,parallel processing ability,and the ability of self organization and self-learning.But when only using ANN to solve a problem,it often has some shortcomings.This paper combines rough set theory with artificial neural networks,applying it in the transformer fault diagnosis.It can fully develop the two methods' advantages,learn from other's strong points to offset one's weakness. Rough set theory can efficiently process the reduction of stylebook collection, so it simplifies the networks' structure,reduces the networks' training epochs and improves the judgement accuracy.Simulation experiment verifies the validity of this method.