RBF Neural Network-Based Wavelet Packet Energy-Aided Fault Localization on a Hybrid Transmission Line

2018 ◽  
pp. 807-815
Author(s):  
Animesh Sarkar ◽  
Bikash Patel
Keyword(s):  
Neural Network ◽  
Transmission Line ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Fault Localization ◽  
Hybrid Transmission

Statement spectrum with two dimensional eigenvalues for intelligent software fault localization

2021 ◽  
pp. 1-16
Author(s):  
Shengbing Ren ◽  
Xing Zuo ◽  
Jun Chen ◽  
Wenzhao Tan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Rbf Neural Network ◽  
Fault Localization ◽  
Test Case ◽  
Two Dimensional ◽  
Intelligent Software ◽  
Localization Model ◽  
Software Fault ◽  
Program Spectrum

The existing Software Fault Localization Frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of fault localization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software fault localization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as fault localization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software fault localization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth.

Four-channel sEMG for Elbow Load Recognition

2021 ◽  
Author(s):  
Jiabin Cai ◽  
Junjun Song ◽  
Yuanqiang Long
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Control Mode ◽  
Support Vector ◽  
Semg Signal ◽  
Joint Force ◽  
Emg Signal ◽  
The Relationship ◽  
Rehabilitation Exercise

Abstract In order to help patients after surgery to carry out reasonable rehabilitation training, avoid joint adhesions and movement disorders, the relationship between surface electromyograph (sEMG) signal changes and the size of the patient ' s joint force in the process of rehabilitation exercise was studied, hoping to use the relationship between them to redesign the control mode of the rehabilitation robot, and a method was proposed to identify the size of the elbow load based on wavelet packet. Firstly, s EMG signals of human elbow joint during stretching and bending under different loads were collected by 4-channel surface electromyography. Then, the wavelet packet decomposition method was used to obtain the feature vector composed of energy(E), variance(VAR) and mean absolute value(MAV) of wavelet packet coefficient. Finally, the improved support vector machine ( ISVM), BP neural network and RBF neural network were used for pattern recognition of three different forces. The experimental results show that the change of sEMG signal is indeed related to the size of joint force. It is feasible to identify the load of s EMG signal.

Diagnosis Technique Based on BP and D-S Theory

2011 ◽  
Vol 179-180 ◽  
pp. 544-548
Author(s):  
Qiu Yun Mo ◽  
Jie Cao ◽  
Feng Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
The Neural Network ◽  
Evidential Theory ◽  
The Given ◽  
Fusion Framework ◽  
Level Fusion ◽  
Theory And Experiments

This paper constructs a common data fusion framework of fault diagnosis, by combining local neural networks with dempster-shafer (D-S) evidential theory. The RBF neural network is proposed as a local neural network of the fault pattern recognition, and its input vectors are extracted by the wavelet packet decomposition of various frequency energy. Then, the signal of each sensor separately has a feature level fusion. This method is effective, verified by experiments. The given decision level fusion is based on combining the features of the neural network and the D-S theory, and experiments show the results of the fault diagnosis are more accurate by this method.

Fault Diagnosis of Cascaded Converter Based on Wavelet Packet RBF Neural Network

2013 ◽  
Vol 273 ◽  
pp. 300-304
Author(s):  
Xin Wang ◽  
Juan Xu ◽  
Guo Dong Zhang ◽  
Rui Min Qi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Open Circuit ◽  
Learning Performance ◽  
Cascaded Converter ◽  
Diagnosis Method ◽  
Faults Diagnosis

To study the power component open circuit faults diagnosis method of the cascaded converter. Aiming at the insufficiency of the BP learning algorithm in the machinery fault diagnosis, such as the low learning convergence speed, the easily appearing local minimum, the instability learning performance caused by the initial value, to proposed a new method applied to the cascaded converter based on radial basis function (RBF) neural network. Experiments show that the method based on wavelet packet analysis and RBF neural network has better learning and fault identification capability, and it can meet the online real-time fault diagnosis of the cascaded converter.

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