Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Neural Network Diagnosis Approach for Analog Circuits
Applied Intelligence
An algorithm for multiple fault diagnosis in analogue circuits: Research Articles
International Journal of Circuit Theory and Applications
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
New approach to predicting proconvulsant activity with the use of Support Vector Regression
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
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The paper is concerned with the application of Support Vector Machine (SVM) to the fault location in the analog electrical circuits. The recognition of fault is based on the measurements of the accessible terminal voltage and current of the network at the set of frequencies. The SVM network is applied as the recognizing system and as the classifier. The important feature of the proposed solution is its high accuracy and great speed of operation. Once the network has been trained, the recognition of fault is achieved immediately, irrespective of the size of the circuit. Thus the solution is suited for real time applications for fault location in electrical circuits. The numerical results of recognition of faulty elements in two different structures of electrical filters are presented and discussed in the paper.