An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Expert Systems with Applications: An International Journal
Decision support systems for time-varying biomedical signals: EEG signals classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Multi-class support vector machine for classification of the ultrasonic images of supraspinatus
Expert Systems with Applications: An International Journal
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Statistics over features of ECG signals
Expert Systems with Applications: An International Journal
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Engineering Applications of Artificial Intelligence
Short-term prediction of air pollution in macau using support vector machines
Journal of Control Science and Engineering - Special issue on Advanced Control in Micro-/Nanosystems
Hi-index | 12.05 |
Engine ignition pattern analysis is one of the trouble-diagnosis methods for automotive gasoline engines. Based on the waveform of the ignition pattern, the mechanic guesses what may be the potential malfunctioning parts of an engine with his/her experience and handbooks. However, this manual diagnostic method is imprecise because many ignition patterns are very similar. Therefore, a diagnosis may need many trials to identify the malfunctioning parts. Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification. To tackle this problem, Wavelet Packet Transform (WPT) is firstly employed to extract the features of the ignition pattern. With the extracted features, a statistics over the frequency subbands of the pattern can then be produced, which can be used by Multi-classLeast Squares Support Vector Machines (MCLS-SVM) for engine fault classification. With the newly proposed classification system, the number of diagnostic trials can be reduced. Besides, MCLS-SVM is also compared with a typical classification method, Multi-layer Perceptron (MLP). Experimental results show that MCLS-SVM produces higher diagnostic accuracy than MLP.