A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers
نویسندگان
1 Department of Mathematics, Payame Noor University (PNU), P.O. Box, 19395-3697, Tehran, Iran
2 Department of Mathematics, Payame Noor University (PNU), P.O. Box 19395-3697, Tehran, Iran
3 Department of Control Engineering, K.N. Toosi University of Technology, Tehran, Iran
doi
10.30473/coam.2019.40779.1083چکیده
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.
