By Professor Seppo J. Ovaska, Professor Les M. Sztandera (auth.)
It is anticipated that using tender computing increases drastically in business purposes, as the conceptual constitution of demanding computing is far too detailed with regards to the nice imprecision of the area round us. This ebook goals at attracting researchers and engineers either within the fields of business electronics (IE) and computational intelligence (CI). by means of imminent the several viewpoints of IE and CI humans, it truly is was hoping to supply practising engineers with new strategies to the difficult real-world difficulties. The functions are divided into different types, electrical energy functions and rising Applications.
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XaT, --d i --. 8) TN Respectively the second model of rotor flux simulator based on the current model, obtained from Eq. 5) is: 50 d [TN ( . ) . 'I' ] -1, -'I' dt- n. = -T,. 9) T,. = x, TN Ir, - rotor time constant. Ixsx" The current model can be written in the following way for neural modeling purposes: 'i'ri (k) where: = (WI 1+ w2J) 'i'ri (k -1) + w31, (k -1), I = [1 0] 01,J = [0 -1]. 10) 10 This model, transformed to the stationary reference (a, 13) co-ordinate system [15], [35] can be treated as a simple connection of two neurons with linear activation functions, as it was illustrated in Fig.
1995), Application of Kohonen's self-organizing network to the diagnosis system for rotating machinery, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Vancouver, Canada, pp. 4039-4044. 23. Nejjari H. H. (1999), Application of fuzzy logic to induction motors condition monitoring, IEEE Power Engineering Review, 19, no. 6, pp. 52-54. 24. , and Vas P. (1995), A fuzzy logic approach to on-line induction motor diagnostics based on stator current monitoring, Proceedings of the Stockholm Powertek, Stockholm, Sweden, pp.
Output Layer Hidden Layer ••• Input Layer Fig. 25. Structure of an Elman neural network model. The trammg algorithm for Elman neural network is similar to BP learning algorithm, both based on the gradient descent principle. However, the roles that the context weights as well as initial context nodes outputs play in the error backpropagation procedure must be taken into consideration in the derivation of this learning algorithm. From Fig. 25, at iteration k, we have the following relationship: (8) where 0 _Ci and net _C i are the output and input of context node i, respectively.
