In collaboration with Payame Noor University and the Iranian Society of Instrumentation and Control Engineers

Document Type : Applied Article


1 Department of Mathematics, Payame Noor University (PNU), P.O. Box 19395-3697, Tehran, Iran

2 Department of Control Engineering, K.N. Toosi University of Technology, Tehran, Iran

3 Department of Mathematics, Payame Noor University (PNU), P.O. Box, 19395-3697, Tehran, Iran


o enhance the performances of rough-neural networks (R-NNs) in the system identification‎,
‎on the base of emotional learning‎, ‎a new stable learning algorithm is developed for them‎. ‎This algorithm facilitates the error convergence by increasing the memory depth of R-NNs‎. ‎To this end‎, ‎an emotional signal as a linear combination of identification error and its differences is used to achieve the learning laws‎.
‎In addition‎, ‎the error convergence and the boundedness of predictions and parameters of the model are proved‎. ‎To illustrate the efficiency of proposed algorithm‎,
‎some nonlinear systems including the cement rotary kiln are identified using this method and the results are compared with some other models.


Main Subjects

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