Journal ArticleDOI
Soft fault detection and isolation in analog circuits: some results and a comparison between a fuzzy approach and radial basis function networks
Marcantonio Catelani,Ada Fort +1 more
TLDR
This paper provides a comparison between two techniques for soft fault diagnosis in analog electronic circuits, based on a fuzzy system, obtained by processing fault dictionary data for automatic generation of IF-THEN rules, and a radial basis function neural network.Abstract:
This paper provides a comparison between two techniques for soft fault diagnosis in analog electronic circuits. Both techniques are based on the simulation before test approach: a "fault dictionary" is a priori generated by collecting, signatures of different fault conditions. Classifiers, trained by the examples contained in the fault dictionary, are then configured to classify the measured circuit responses. The suggested classifiers have similar structures. The first is based on a fuzzy system, obtained by processing fault dictionary data for automatic generation of IF-THEN rules, and the second classifier is based on a radial basis function neural network. The two classifiers are used to detect and isolate faults both at the subsystem and component levels. The experimental results point out that both classifiers provide low classification errors in the presence of noise and nonfaulty components tolerance effects. The fuzzy approach provides better results due to an efficient generation method for the IF-THEN rules that allows adding IF parts in the input space regions where ambiguity occurs.read more
Citations
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Journal ArticleDOI
RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits
D. Binu,B. S. Kariyappa +1 more
TL;DR: A technique for the fault diagnosis in analog circuits is designed by proposing a new optimization algorithm, named, rider optimization algorithm (ROA), based on a group of riders, racing toward a target location.
Journal ArticleDOI
Diagnostics and Prognostics Method for Analog Electronic Circuits
TL;DR: This work has developed a method for detecting faulty circuit condition, isolating fault locations, and predicting the remaining useful performance of analog circuits through the successive refinement of the circuit's response to a sweep signal.
Journal ArticleDOI
Wavelet neural network approach for fault diagnosis of analogue circuits
TL;DR: A systematic method for fault diagnosis of analogue circuits based on the combination of neural networks and wavelet transforms is presented and the reliability of the method and comparison with other methods is shown.
Journal ArticleDOI
A survey on fault diagnosis of analog circuits: Taxonomy and state of the art
D. Binu,B. S. Kariyappa +1 more
TL;DR: This critical review discusses the research challenges that are still available in the existing techniques and the way to extend the current research is also examined.
Journal ArticleDOI
Automated selection of test frequencies for fault diagnosis in analog electronic circuits
TL;DR: This paper suggests three novel methods for selecting the frequencies of sinusoidal test signals to be used in fault diagnosis of analog electronic circuits based on a sensitivity analysis and shows to be particularly effective in linear circuits.
References
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Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI
Fast learning in networks of locally-tuned processing units
John Moody,Christian J. Darken +1 more
TL;DR: This work proposes a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988).
Journal ArticleDOI
Networks for approximation and learning
Tomaso Poggio,Federico Girosi +1 more
TL;DR: Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks as mentioned in this paper, and two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering.
Journal Article
Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
David S. Broomhead,David Lowe +1 more
TL;DR: The relationship between 'learning' in adaptive layered networks and the fitting of data with high dimensional surfaces is discussed, leading naturally to a picture of 'generalization in terms of interpolation between known data points and suggests a rational approach to the theory of such networks.