Artificial Intelligence
Fundamentals of digital image processing
Fundamentals of digital image processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the theory of neural computation
Introduction to the theory of neural computation
A spectrum of logical definitions of model-based diagnosis
Computational Intelligence
Guide to Neural Computing Applications
Guide to Neural Computing Applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks for Multiple Fault Diagnosis in Analog Circuits
Proceedings of the IEEE International Workshop on Defect and Fault Tolerance in VLSI Systems
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
A signal-processing tool for non-destructive testing of inaccessible pipes
Engineering Applications of Artificial Intelligence
ISTASC'05 Proceedings of the 5th WSEAS/IASME International Conference on Systems Theory and Scientific Computation
Support Vector Machine for soft fault location in electrical circuits
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Fault detection in analog circuits using a fuzzy dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
A new multi-valued neural network for the extraction of lumped models of analog circuits
Analog Integrated Circuits and Signal Processing
| Hi-index | 0.00 |
This paper presents a neural network system for the diagnosis of analogcircuits and shows how the performance of such a system can be affected by the choice of differenttechniques used by its submodules. In particular we discuss the influenceof feature extraction techniques such as Fourier Transforms, Wavelets and Principal ComponentAnalysis. The system uses several different power supplies and as manyneural networks “in parallel”. Two different algorithms that canbe used to combine the candidate sets produced by each network arealso presented. The system is capable of diagnosing multiplefaults even if trained on single ones.