Non linear support vector machine based partial discharge patterns recognition using fractal features
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Summary
The non linear SVM with semi variance method provides outer performance as compared with other methods due to its gain flexibility and good out-of-sample generalization.
- Type
- article
- Published
- 2014-09-01
- Cited by
- 14
- References
- 25
- OpenAlex
- https://openalex.org/W1934321774
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:45339684
Keywords
Fractal, Support vector machine, Pattern recognition (psychology), Artificial intelligence, Partial discharge
References
- A user's guide to support vector machines.
- Nonlinear kernel-based statistical pattern analysis
- Pattern-recognition transforms
- A heuristic complex probabilistic neural network system for partial discharge pattern classification
- Kernel Optimization in Discriminant Analysis
- Partial Discharge Pattern Recognition Using Radial Basis Function Neural Network
- Partial discharge source classification and de-noising in rotating machines using Discrete Wavelet Transform and directional coupling capacitors
- Partial discharge signal denoising with spatially adaptive wavelet thresholding and support vector m
- Partial discharge pulse pattern recognition using Hidden Markov Models
- Development of automatic identification method for GIS PD (Partial Discharge) defects diagnosis
- PD recognition with knowledge-based preprocessing and neural networks
- The use of fractal features for recognition of 3-D discharge patterns
- Partial discharge image recognition using a new group of features
- The importance of statistical characteristics of partial discharge data
- Classification of partial discharges
- In Defense of One-Vs-All Classification
- Fuzzy logic applied to PD pattern classification
- Partial discharge recognition through an analysis of SF6 decomposition products part 2: feature extraction and decision tree-based pattern recognition
- Fundamentals in computer aided PD processing, PD pattern recognition and automated diagnosis in GIS
- PD pattern recognition for stator bar models with six kinds of characteristic vectors using BP network
Cited by
- An Advanced Partial Discharge Recognition Strategy of Power Cable
- Priori Information Based Support Vector Regression and Its Applications
- Electric field and electric potential due to a finite cylindrical surface charge distribution considering a linearly variable surface charge density
- Usage of antenna for detection of tree falls on overhead lines with covered conductors
- Recognition of multiple partial discharge patterns by multi‐class support vector machine using fractal image processing technique
- Partial discharge pattern analysis using multi-class support vector machine to estimate cavity size and position in solid insulation
- Research on transfer learning algorithm based on support vector machine
- Recognition of Single and Multiple Partial Discharge patterns using Deep Learning Algorithm
- Recognition of shed damage on 11-kV polymer insulator using Bayesian optimized convolution neural network
- Recognition of Fused Partial Discharge Patterns in High Voltage Insulation Systems: A Hybrid DCNN and SVM Based Approach
- A Transfer Learning Algorithm Based on Support Vector Machine
- Recognition of Power Transformer Defect Identification Based on Dissolved Gas Analysis using Support-Vector Machine Approach
- Partial Discharge Pattern Recognition for XLPE Cables Based on Autonomous Feature Extraction of CNN
- An efficient intrusion detection method using federated transfer learning and support vector machine with privacy-preserving
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