scholarly journals Separations and Feature Extractions for Image Signals Using Independent Component Analysis Based on Neural Networks of Efficient Learning Rule

2003 ◽  
Vol 13 (2) ◽  
pp. 200-208
Author(s):  
Yong-Hyun Cho
Keyword(s):  
Neural Networks ◽  
Independent Component Analysis ◽  
Learning Rule ◽  
Component Analysis ◽  
Independent Component ◽  
Efficient Learning

An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks

2019 ◽  
Vol 68 (5) ◽  
pp. 1353-1361 ◽  
Author(s):  
Juan Enrique Garcia-Bracamonte ◽  
Juan Manuel Ramirez-Cortes ◽  
Jose de Jesus Rangel-Magdaleno ◽  
Pilar Gomez-Gil ◽  
Hayde Peregrina-Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component

scholarly journals Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

Sensors ◽  
2012 ◽  
Vol 12 (6) ◽  
pp. 8055-8072 ◽  
Author(s):  
Teodoro Aguilera ◽  
Jesús Lozano ◽  
José A. Paredes ◽  
Fernando J. Álvarez ◽  
José I. Suárez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Independent Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Electronic Nose ◽  
Component Analysis ◽  
Independent Component ◽  
Artificial Neural

Application of Independent Component Analysis in Machine Fault Diagnosis

2014 ◽  
Vol 905 ◽  
pp. 524-527
Author(s):  
Feng Miao ◽  
Rong Zhen Zhao
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Learning Rule ◽  
Component Analysis ◽  
Independent Component ◽  
Machine Fault Diagnosis ◽  
Machine Fault

A novel fast algorithm for lndependent Component Analysis is introduced, which can be used for blind source separation and machine fault diagnosis feature extraction. It is shown how a neural network learning rule can be transformed into a fixed-point iteration, which provides an algorithm that is very simple, does not depend on any user-defined parameters, and is fast to converge to the most accurate solution allowed by the data. The purpose of this paper is to review the application of blind source separation in the machine fault diagnosis,including the following aspects: noise elimination and extraction of the weak signals,the separation of multi-fault sources,redundancy reduction,feature extraction and pattern classification based on independent component analysis. And its application in machine fault diagnosis is illustrated by the examples. In addition, some prospects about using blind source separation for machine fault diagnosis are discussed.

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