scholarly journals Study on Classification of EEG Signals Based on Wavelet Transformation and BP Neural Network

2015 ◽  
Author(s):  
Zhulin Yu ◽  
Bing Zhao ◽  
Jie Liu ◽  
Mingtao Yu ◽  
Ling Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Wavelet Transformation ◽  
Eeg Signals

Classification of heart sound signals with BP neural network and logistic regression

2017 ◽  
Author(s):  
Lina Li ◽  
Xinpei Wang ◽  
Xiaping Du ◽  
Yuanyuan Liu ◽  
Changchun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Bp Neural Network ◽  
Heart Sound

Research on Automatic Flaw Classification and Feature Extraction of Ultrasonic Testing

2008 ◽  
Vol 381-382 ◽  
pp. 631-634 ◽  
Author(s):  
Jian Li ◽  
X. Zhan ◽  
J. Zhuge ◽  
Z. Zeng ◽  
Shi Jiu Jin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Bp Network ◽  
Local Characteristics ◽  
Computational Speed ◽  
Ultrasonic Echo ◽  
Flaw Classification ◽  
Girth Welds ◽  
Flaw Type

In this paper, Lifted Wavelet Transform (LWT) and BP neural network are used for automatic flaw classification of pipeline girth welds. LWT is proposed to extract flaw feature from ultrasonic echo signals, ideally matched local characteristics of original signal and increasing the computational speed and flaw classification efficiency. After extracting features of all flaw echoes, a feature library is constructed. A modified BP neural network is followed as a classifier, trained by the library. When feature of any flaw echo is extracted and sent to BP network, flaw type is the output, realizing automatic flaw classification. Experiment results prove the proposed method, LWT with BP neural network, is more fit for automatic flaw classification than traditional methods.

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