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Faults diagnosis of stochastic dynamic systems based on neural network probability density function estimation

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2 Author(s)
Grishin, Y. ; Fac. of Electr. Eng., Bialystok Tech. Univ., Poland ; Konopko, K.

The paper presents a fault diagnosis algorithm based on multidimensional probability density function (pdf) estimation which is suitable for stochastic nonlinear systems. The pdf of symptom vector is estimated with use of the Radial-Basis Function (RBF) and Hyperradial-Basis Function (HRBF) artificial neural networks (NN). The numerical example of diagnosis of a nonlinear system is presented. The influences of the NN parameters and learning on the algorithm performance are discussed.

Published in:

Electrotechnical Conference, 2004. MELECON 2004. Proceedings of the 12th IEEE Mediterranean  (Volume:1 )

Date of Conference:

12-15 May 2004

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