HVDC transmission line fault localization base on RBF neural network with wavelet packet decomposition

2015 ◽  
Author(s):  
Hang Cui ◽  
Niannian Tu
Keyword(s):  
Neural Network ◽  
Transmission Line ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Fault Localization ◽  
Wavelet Packet Decomposition ◽  
Hvdc 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.

Intelligent Identification of Flow Regime Based on a Novel Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1715-1718
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Flow Regime ◽  
Recurrent Neural Network ◽  
Pressure Difference ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Elman Neural Network ◽  
Flow Regime Identification ◽  
Novel Method

A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

Research of Fault Diagnosis Method Based on Improved Extreme Learning Machine

2015 ◽  
Vol 727-728 ◽  
pp. 872-875
Author(s):  
Wen Bo Na ◽  
Qing Feng Jiang ◽  
Zhi Wei Su
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Rbf Neural Networks ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Better Than

In order to improve the accuracy of diagnosis pumping, and accelerate the speed of diagnosis, a fault diagnosis model based on improved extreme learning machine (RWELM) was proposed. Firstly, it extracted the energy characteristic eigenvector of dynamometer cards of an oilfield in northern Shanxi by using wavelet packet decomposition method. Then through simulation of fault diagnosis, and compare with the extreme learning machine (ELM), RBF neural networks and support vector machine (SVM). The experimental results show that the accuracy and the speed of fault diagnosis based on the RWELM are better than the ELM, RBF neural network and SVM.

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.

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