Neural networks and the bias/variance dilemma
Neural Computation
On minimal set of test nodes for fault dictionary of analog circuit fault diagnosis
Journal of Electronic Testing: Theory and Applications
A Combined Clustering and Neural Network Approach for Analog Multiple Hard Fault Classification
Journal of Electronic Testing: Theory and Applications
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Neural network-based analog fault diagnosis using testability analysis
Neural Computing and Applications
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Wind turbines fault diagnosis using ensemble classifiers
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Multiple catastrophic fault diagnosis of analog circuits considering the component tolerances
International Journal of Circuit Theory and Applications
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A new neural network-based analog fault diagnosis strategy is introduced. Ensemble of neural networks is constructed and trained for efficient and accurate fault classification of the circuit under test (CUT). In the testing phase, the outputs of the individual ensemble members are combined to isolate the actual CUT fault. Prominent techniques for producing the ensemble are utilized, analyzed and compared. The created ensemble exhibit high classification accuracy even if the CUT has overlapping fault classes which cannot be isolated using a unitary neural network. Each neural classifier of the ensemble focuses on a particular region in the CUT measurement space. As a result, significantly better generalization performance is achieved by the ensemble as compared to any of its individual neural nets. Moreover, the selection of the proper architecture of the neural classifiers is simplified. Experimental results demonstrate the superior performance of the developed approach.