A novel hybrid intelligent fault diagnosis method based on improved RBF neural network
- https://doi.org/10.2991/icemi-16.2016.49How to use a DOI?
- differential evolution, RBF neural network, intelligent fault diagnosis.
The radial basis function neural network (RBFNN) is a great potential artificial intelligence technology and can effectively realize the fault diagnosis for small sample and nonlinear problem. But the parameters of RBFNN model seriously affects the generalization ability and diagnosis accuracy on the great extent. So an improved differential evolution algorithm based on dynamic adaptive adjustment strategy is proposed to optimize the parameters of RBFNN model for obtaining the optimal RBFNN(DASDERBFNN) method. Then the proposed DASDERBFNN method is used to construct a new fault diagnosis (DSDRBFNFD) method. The experiment results show that the proposed DSDRBFNFD method can obtain the higher accuracy of fault diagnosis and is effective fault diagnosis for the engine.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yi Liu PY - 2016/07 DA - 2016/07 TI - A novel hybrid intelligent fault diagnosis method based on improved RBF neural network BT - Proceedings of the 2016 International Conference on Economics and Management Innovations PB - Atlantis Press SP - 241 EP - 245 SN - 2352-538X UR - https://doi.org/10.2991/icemi-16.2016.49 DO - https://doi.org/10.2991/icemi-16.2016.49 ID - Liu2016/07 ER -