Self-organizing neural network for fault location in electrical circuits
Stanisław Osowski , Krzysztof Siwek
AbstractA novel approach to fault location in analog dynamic circuits based on the application of self-organizing neural network has been presented. Important features are a very good generalization property and fast speed. Once the network has been trained, the recognition of the fault is done immediately, irrespective of the size of the circuit. The network is able to detect faults in the nonideal circuit, in which the tolerance of elements is taken into account. Two cases of analog circuits have been simulated and checked: the RLC circuit at multiple measurement points and the measurement done at external nodes of the circuit for multiple frequencies. The results of numerical experiments are given and discussed
|Book||1998 IEEE International Conference on Electronics, Circuits and Systems, vol. 2, 1998|
|Keywords in English||analog dynamic circuits, analogue circuits, biquadratic filters, circuit analysis computing, computer modelling, external nodes, fault location, fault recognition, generalisation (artificial intelligence), generalization property, Kohonen network, ladder networks, learning (artificial intelligence), multiple measurement points, nonideal circuit, RC active biquadratic filter, RC circuits, RLC ladder circuit, self-organising feature maps, self-organizing neural network, tolerance|
|Citation count*||3 (2015-01-27)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.