The application of RBF network based on the principle of immune in the condenser´s fault diagnosis

Author

Tie-sheng, Wang ; Shan-Shan, Li

Author_Institution

North China Inst. of Water Conservancy & Hydroelectric Power, Zhengzhou, China

Volume

2

fYear

2010

fDate

25-27 June 2010

Abstract

For the specific problems which is determined by the Center value and the width of the RBF neural network function, a new network training method is proposed. It is based on the immune theory and combines immune genetic algorithm with RBF neural network. And it is applied to realize the failure diagnosis of condenser. The result of verification shows that the proposed method can improve the accuracy and the speed of failure diagnosis effectively and has certain application value in engineering.

Keywords

artificial immune systems; condensers (steam plant); fault diagnosis; genetic algorithms; learning (artificial intelligence); power engineering computing; radial basis function networks; RBF network; condenser fault diagnosis; immune genetic algorithm; immune theory; network training method; radial basis function network; Algorithm design and analysis; Application software; Biological neural networks; Computer networks; Fault diagnosis; Neural networks; Power system modeling; Radial basis function networks; Turbines; Water conservation; RBF network; condenser; failure diagnosis; immune;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Design and Applications (ICCDA), 2010 International Conference on

Conference_Location

Qinhuangdao

Print_ISBN

978-1-4244-7164-5

Electronic_ISBN

978-1-4244-7164-5

Type

conf

DOI

10.1109/ICCDA.2010.5541505

Filename

5541505