Application of RBF neural network based on AP clustering in Engine Fault Diagnosis

被引：0

作者：

Wu Shi-li ^{[1
]}

Tang Zhen-min ^{[1
]}

Liu Yong ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER) | 2015年

关键词：

Fault Diagnosis; AP clustering; RBF neural network;

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：

RBF neural network is widely used in intelligent fault diagnosis with its good performance for nonlinear problems. But the nodes number in hidden layer is difficult to get, so the advanced RBF neural network (AP-RBF) based on AP clustering is proposed to gain proper hidden layer efficiently. In AP-RBF, the exemplars obtained by AP clustering are used to construct hidden layer of RBF network. The results of engine fault diagnosis show that AP-RBF can achieve higher accuracy through more compact hidden layer than traditional RBF and RBF based on subtractive clustering (C-RBF).

引用

页码：472 / 475

页数：4

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