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This paper deals with the application of the Radial Basis Function (RBF) networks for the induction motor fault detection. The rotor faults are analysed and fault symptoms are described. Next the main stages of the design methodology of the RBF-based neural detectors are described. These networks are trained and tested using measurement data of the stator current (MCSA). The efficiency of developed...
The paper deals with the application of neural networks for state variables estimation of the electrical drive system with an elastic joint. The torsional vibration suppression of such drive system is achieved by the application of a special control structure with a state-space controller and additional feedbacks from mechanical state variables. Signals of the torsional torque and the load-machine...
The paper deals with the application of the feed-forward and cascade-forward neural networks to mechanical state variable estimation of the drive system with elastic coupling. The learning procedure of neural estimators is described and the influence of the input vector size and neural network structure to the accuracy of state variable estimation is investigated. The quality of state estimation by...
The propagation of EEG activity during the Continuous Attention Test (CAT) was determined by means of Short-time Directed Transfer Function (SDTF). SDTF supplied the information on the direction, spectral content and time evolution of the propagating EEG activity. The differences in propagation for target and non-target conditions were found mainly in the frontal structures of the brain.
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