The nature of statistical learning theory
The nature of statistical learning theory
Expert Systems with Applications: An International Journal
Investigation of engine fault diagnosis using discrete wavelet transform and neural network
Expert Systems with Applications: An International Journal
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fault diagnosis of pneumatic systems with artificial neural network algorithms
Expert Systems with Applications: An International Journal
Fault diagnosis of power transformer based on support vector machine with genetic algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Fuzzy reasoning spiking neural P system for fault diagnosis
Information Sciences: an International Journal
Information Sciences: an International Journal
Hi-index | 12.05 |
This paper introduces multiclass support vector machines (SVM) and a back-propagation neural network (BPNN) for fault diagnosis of a field air defense gun. These intelligent methods preclude human error in fault diagnosis, and they make it possible to diagnose a new failure precisely and rapidly. Our experimental results show that both SVM and BPNN provide excellent fault diagnosis accuracy when sufficient training samples are examined, and multiclass SVM models have better fault diagnosis accuracy than BPNN models when numbers of training sets are small. Our multiclass SVM approach also offers advantages of solution stability and requires fewer control parameters; it is easier to apply it to fault diagnosis problems than BPNN.