@article { author = {Ahmadi, Ghasem and Teshnehlab, Mohammad and Soltanian, Fahimeh}, title = {A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers}, journal = {Control and Optimization in Applied Mathematics}, volume = {3}, number = {1}, pages = {87-108}, year = {2018}, publisher = {Payame Noor University (PNU)}, issn = {2383-3130}, eissn = {2538-5615}, doi = {10.30473/coam.2019.40779.1083}, abstract = {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.}, keywords = {Rough-neural network‎,‎System identification‎,‎Emotional learning‎,‎Lyapunov stability theory}, url = {https://mathco.journals.pnu.ac.ir/article_6292.html}, eprint = {https://mathco.journals.pnu.ac.ir/article_6292_b42dbf9ad36d3657c67453806d638088.pdf} }