Application of the variable architecture BP neural network in fault diagnosis of control system

Author

Yuanping, Ni

Author_Institution

Sch. of Electron. Eng., Yunnan Polytech. Univ., Kunming, China

fYear

1996

fDate

2-6 Dec 1996

Firstpage

736

Lastpage

739

Abstract

In this paper a variable architecture BP neural network model is proposed. This new model possesses advantages over the previous one in operation speed, learning ability and fault tolerance to input information. It can be used to promote the recognizing rate of fault pattern and decrease the neural networks learning time in fault diagnosis of control system. The experimental results show that the model is feasible and effective

Keywords

backpropagation; control systems; fault diagnosis; feedforward neural nets; neural net architecture; pattern recognition; BP neural network; backpropagation; control system; fault diagnosis; fault tolerance; learning; multilayer neural networks; pattern recognition; variable architecture; Artificial neural networks; Control system synthesis; Control systems; Electric variables control; Fault diagnosis; Intelligent networks; Multi-layer neural network; Neural networks; Neurons; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Technology, 1996. (ICIT '96), Proceedings of The IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

0-7803-3104-4

Type

conf

DOI

10.1109/ICIT.1996.601693

Filename

601693

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3277279