The nature of statistical learning theory
The nature of statistical learning theory
The Fractional Wave Packet Transform
Multidimensional Systems and Signal Processing - Special issue on recent developments in time-frequency analysis
Journal of Electronic Testing: Theory and Applications
Journal of Electronic Testing: Theory and Applications
Classification of Defective Analog Integrated Circuits Using Artificial Neural Networks
Journal of Electronic Testing: Theory and Applications
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Domain described support vector classifier for multi-classification problems
Pattern Recognition
Expert Systems with Applications: An International Journal
Fabric defect detection based on multiple fractal features and support vector data description
Engineering Applications of Artificial Intelligence
Fault classifier of rotating machinery based on weighted support vector data description
Expert Systems with Applications: An International Journal
Discrete fractional Fourier transform based on orthogonalprojections
IEEE Transactions on Signal Processing
Density-Induced Support Vector Data Description
IEEE Transactions on Neural Networks
A New Optimal Test Node Selection Method for Analog Circuit
Journal of Electronic Testing: Theory and Applications
SVM-SVDD: a new method to solve data description problem with negative examples
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Density weighted support vector data description
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, a new approach of fault diagnosis in analog circuits, which employs the Fractional Wavelet Transform (FWT) to extract fault features and adopts a fuzzy multi-classifier based on the Support Vector Data Description (SVDD) to diagnose circuit faults, is proposed. Firstly, a discrete FWT algorithm by the fractional kernel matrix is performed to preprocess fault samples. To obtain the optimal fractional order, two methods trained with the genetic algorithm are introduced. One approach is performed by the best diagnostic result, and the other is based on the maximum among-cluster center distance by the Kernel Fuzzy C-Means (KFCM) algorithm. In this paper, a threshold value is used to decrease the fuzzy region which in the overlap between hyperspheres of SVDD. Then, a SVDD fuzzy multi-classifier is applied to diagnose faults in analog circuit, and fuzzy faults are diagnosed in fuzzy sets by the relative distance. The simulation results show that the FWT succeeds in extracting local fault features and the classifier effectively detects faults.