Fault Diagnosis for Embedded System Based on Neural Network

被引：0
|
作者：
Du, G. C. [1 ]
机构：
[1] Yellow River Conservancy Tech Inst, Kaifeng, Peoples R China
来源：
INTERNATIONAL CONFERENCE ON ADVANCED EDUCATIONAL TECHNOLOGY AND INFORMATION ENGINEERING (AETIE 2015) | 2015年
关键词：
embedded system; RBF neural network; fault diagnosis; particle swarm optimization algorithm;
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：
In the paper, Radial Basis Function (RBF) neural network optimized by particle swarm optimization algorithm is proposed to fault diagnosis for embedded system. The parameters of Radial Basis Function (RBF) neural network are selected by particle swarm optimization algorithm to solve the problem of the parameters selection of Radial Basis Function (RBF) neural network. In order to testify the superiority of Radial Basis Function (RBF) neural network optimized by particle swarm optimization algorithm in fault diagnosis for embedded system, traditional Radial Basis Function (RBF) neural network and Back Propagation (BP) neural network are applied to compare with the proposed Radial Basis Function (RBF) neural network optimized by particle swarm optimization algorithm. The comparison results show that the diagnosis accuracy of Radial Basis Function (RBF) neural network optimized by particle swarm optimization algorithm is the best methods among traditional Radial Basis Function (RBF) neural network, Back Propagation (BP) neural network and Radial Basis Function (RBF) neural network optimized by particle swarm optimization algorithm.
引用
收藏
页码：674 / 680
页数：7
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