Feature extract based on improved fuzzy neural network

Author

Chang, Che ; Dan, Hu

Author_Institution

Sch. of Mech. Eng. & Autom., Xihua Univ., Chengdu, China

fYear

2010

fDate

26-28 May 2010

Firstpage

2244

Lastpage

2246

Abstract

By changing the construction of the fuzzy neural network based on wavelet basis function, an improved fuzzy neural network is introduced for feature extraction of fault information. By the selection of wavelet base and the orthogonal least-square (OLS) algorithm, an improved fuzzy neural network is described for feature extraction so as to improve the system convergence. Finally, according to the cutting force sample data, it is proved that this method has fast convergence and excellent optimization feature subset property, reduces the significant over-fitting problem.

Keywords

fault diagnosis; feature extraction; fuzzy neural nets; least squares approximations; cutting force sample data; fault information; feature extraction; improved fuzzy neural network; orthogonal least-square algorithm; system convergence; wavelet basis function; Clustering algorithms; Convergence; Data mining; Diagnostic expert systems; Fault diagnosis; Feature extraction; Fuzzy neural networks; Fuzzy sets; Multiresolution analysis; Neural networks; Cutting Force; Feature Extract; Neural Network; Wavelet Function;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498832

Filename

5498832