A novel hybrid intelligent fault diagnosis method based on improved RBF neural network

DOI

10.2991/icemi-16.2016.49How to use a DOI?

Keywords

differential evolution, RBF neural network, intelligent fault diagnosis.

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/icemi-16.2016.49
ID  - Liu2016/07
ER  -