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The Design of Optimized Type-2 Fuzzy Neural Networks and Its Application  

Kim, Gil-Sung (수원대학교 전기공학과)
Ahn, Ihn-Seok (위덕대학교 에너지전기공학부)
Oh, Sung-Kwun (수원대학교 전기공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.58, no.8, 2009 , pp. 1615-1623 More about this Journal
Abstract
In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, we introduce Type-2 Fuzzy Neural Networks (T2FNNs) optimized by means of Particle Swarm Optimization(PSO). T2FNNs exploit Type-2 fuzzy sets which have a characteristic of robustness in the diverse area of intelligence systems. Considering the on-site situation where it is not easy to obtain voltage phases to be used for PRPDA (Phase Resolved Partial Discharge Analysis), the PD data sets measured in the laboratory were artificially changed into data sets with shifted voltage phases and added noise in order to test the proposed algorithm. Also, the results obtained by the proposed algorithm were compared with that of conventional Neural Networks(NNs) as well as the existing Radial Basis Function Neural Networks (RBFNNs). The T2FNNs proposed in this study were appeared to have better performance when compared to conventional NNs and RBFNNs.
Keywords
Pattern Recognition; Partial Discharge; Dielectric Degradation; Type-2 Fuzzy Logic Systems; Type-2 Fuzzy Neural Networks; Particle Swarm Optimization; PRPDA;
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