Abstract
A neural network based knowledge discovery method for single fault detection in electronics circuits is presented. A functional equivalence of Radial Basis Function (RBF) neural network and Takagi-Sugeno (TS) fuzzy system is used in this process. A specially modified incremental RBF network training scheme suitable for rule discovery is used. Next, the RBF neural network is converted into the TS fuzzy system. A set of linguistic rules for detection of circuit catastrophic faults are obtained (100% detection accuracy was achieved for the tested electronic circuit).
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Koszlaga, J., Strumillo, P. (2004). Discovery of Linguistic Rules by Means of RBF Network for Fault Detection in Electronic Circuits. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_29
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DOI: https://doi.org/10.1007/978-3-540-24844-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
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