scholarly journals Fault Diagnosis for Wireless Sensor by Twin Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Mingli Ding ◽  
Dongmei Yang ◽  
Xiaobing Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Technique ◽  
Large Data ◽  
Wireless Sensor ◽  
Sensor Technology ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Data Sets ◽  
Training Procedure

Various data mining techniques have been applied to fault diagnosis for wireless sensor because of the advantage of discovering useful knowledge from large data sets. In order to improve the diagnosis accuracy of wireless sensor, a novel fault diagnosis for wireless sensor technology by twin support vector machine (TSVM) is proposed in the paper. Twin SVM is a binary classifier that performs classification by using two nonparallel hyperplanes instead of the single hyperplane used in the classical SVM. However, the parameter setting in the TSVM training procedure significantly influences the classification accuracy. Thus, this study introduces PSO as an optimization technique to simultaneously optimize the TSVM training parameter. The experimental results indicate that the diagnosis results for wireless sensor of twin support vector machine are better than those of SVM, ANN.

scholarly journals A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 960 ◽  
Author(s):  
Fang Yuan ◽  
Jiang Guo ◽  
Zhihuai Xiao ◽  
Bing Zeng ◽  
Wenqiang Zhu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Chemical Reaction ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Chemical Reaction Optimization ◽  
Model Based ◽  
Reaction Optimization ◽  
Diagnosis Model ◽  
Transformer Fault

The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

Nonlinear clustering-based support vector machine for large data sets

2008 ◽  
Vol 23 (4) ◽  
pp. 533-549 ◽  
Author(s):  
Yongqiao Wang ◽  
Xun Zhang ◽  
Souyang Wang ◽  
K.K. Lai
Keyword(s):  
Support Vector Machine ◽  
Large Data ◽  
Large Data Sets ◽  
Support Vector ◽  
Data Sets

Adapted Infinite Kernel Learning by Multi-Local Algorithm

2016 ◽  
Vol 30 (04) ◽  
pp. 1651004 ◽  
Author(s):  
Süreyya Özöğür Akyüz ◽  
Gürkan Üstünkar ◽  
Gerhard Wilhelm Weber
Keyword(s):  
Model Selection ◽  
Optimization Technique ◽  
Multiple Kernel Learning ◽  
Large Data ◽  
Optimization Methods ◽  
Test Point ◽  
Kernel Learning ◽  
Local Algorithm ◽  
Support Vector ◽  
Data Sets

The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, observed margin strategy is integrated into our novel infinite kernel learning (IKL) algorithm together with multi-local procedure (MLP) which is an optimization technique to find global solution. The experimental results show improvements in accuracy and speed when comparing with multiple kernel learning (MKL) and semi-infinite linear programming (SILP) with CV.

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