Fault Diagnosis Based on Genetic Algorithm for Optimization of EBF Neural Network

被引：0

作者：

Wang, Yahui ^{[1
]}

Huo, Yifeng ^{[1
]}

机构：

[1] Beijing Univ Civil Engn & Architechture, Sch Elect & Informat Engn, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012) | 2012年

关键词：

Ellipsoidal basis function; Genetic algorithm; Fault diagnosis;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：3205 / 3207

页数：3