Soft Computing in Industrial Electronics by Professor Seppo J. Ovaska, Professor Les M. Sztandera

By Professor Seppo J. Ovaska, Professor Les M. Sztandera (auth.)

It is anticipated that using gentle computing increases vastly in commercial functions, as the conceptual constitution of challenging computing is far too distinctive in terms of the nice imprecision of the realm round us. This e-book goals at attracting researchers and engineers either within the fields of commercial electronics (IE) and computational intelligence (CI). through impending the various viewpoints of IE and CI humans, it truly is was hoping to supply practising engineers with new suggestions to the challenging real-world difficulties. The functions are divided into different types, electrical strength functions and rising Applications.

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I) . From (9) to (11), it is clear that the initial outputs of context nodes, 0 _ ci(1) , have an important contribution to the output of Elman neural network. Further, based on (18), they play an indispensable role in the learning algorithm. Consequently, the overall performance of Elman neural network could be enhanced by separately optimizing 0 _ci(1) in some way. Due to its powerful dynamical properties, Elman neural network has found numerous applications in such areas as time series prediction, system identification, and adaptive control.

Also, new high performance speed sensorless 1M control schemes, based on FOC and DTC, are described. The simulation and experimental oscillograms that illustrate the performance of the discussed NN based control techniques are shown. 1 General Problems of Speed Estimation in the Induction Motor Drives Mechanical variables such as the rotor speed or position are usually measured by tachogenerators or incremental encoders and resolvers. As it was said before, these sensors mounted in the drive system cause higher installation cost, increase size of the drive motor, need the sensor cable and decrease reliability of the drive system [11], [27], [35].

6, pp. 448-453. 19. e. (1993), On the application of artificial neural networks for motor fault detection--Parts I and II, IEEE Transactions on Industrial Electronics, 40, no. 2, pp. 181-196. 20. , and Tassoni e. (1995), Neural networks aided on-line diagnostics of induction motor rotor faults, IEEE Transactions on Industry Applications, 31, no. 4, pp. 892-899. 21. Penman J. M. (1994), Feasibility of using unsupervised learning artificial neural networks for the condition monitoring of electrical machines, lEE Proceedings, Part B, 141, pp.

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