Fault diagnosis based on genetic algorithm for optimization of EBF neural network

Author

Wang, Yahui ; Huo, Yifeng

Author_Institution

Sch. of Electr. & Inf. Eng., Beijing Univ. of Civil Eng. & Archit., Beijing, China

fYear

2012

fDate

6-8 July 2012

Firstpage

3205

Lastpage

3207

Abstract

Ellipsoidal basis function(EBF) can make the partition and limitary of input space. Compared with the Guassian function of radial basis function(RBF) neural network, the EBF can make the partition of input space more specific, which has the higher capability of pattern recognition. However, the neural network has a common problem of training the weight and threshold. The evolution of genetic algorithm(GA) can maximumly optimize the training time of neural network. In this paper, a new method based on GA-EBF neural network was proposed. The simulation experiment shows that the proposed method has a higher rate of fault diagnosis than that of RBF neural network.¹

Keywords

fault diagnosis; genetic algorithms; pattern recognition; radial basis function networks; EBF neural network; GA; ellipsoidal basis function; fault diagnosis; genetic algorithm; optimization; pattern recognition; Biological neural networks; Educational institutions; Ellipsoids; Fault diagnosis; Genetic algorithms; Support vector machines; Ellipsoidal basis function; Fault diagnosis; Genetic algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2012 10th World Congress on

Conference_Location

Beijing

Print_ISBN

978-1-4673-1397-1

Type

conf

DOI

10.1109/WCICA.2012.6358425

Filename

6358425