Title :
Feature extract based on improved fuzzy neural network
Author :
Chang, Che ; Dan, Hu
Author_Institution :
Sch. of Mech. Eng. & Autom., Xihua Univ., Chengdu, China
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;
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
DOI :
10.1109/CCDC.2010.5498832