عنوان	Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Recurrent RBFN Type-2 Fuzzy sets Self-evolving System identification
چکیده	This paper presents a new self-evolving recurrent Type-2 Fuzzy Radial Basis Function Network (T2FRBFN) in which the weights are considered Gaussian type-2 fuzzy sets and uncertain mean in each RBF neuron. The capability of the proposed T2FRBFN for function approximation and dynamical system identification perform better than the conventional RBFN. A novel type-2 fuzzy clustering is presented to add or remove the hidden RBF neurons. For parameter learning, back-propagation with adaptive learning rate is used. Finally the proposed T2FRBFN is applied to identification of three nonlinear systems as case studies. A comparison between T2FRBFN and the conventional RBFN as well as the method of Rubio-Solis and Panoutsos (2015) is presented. Simulation results and their statistical description show that the proposed T2FRBFN perform better than the conventional RBFN.
پژوهشگران	جعفر طاووسی (نفر اول)، امیر ابوالفضل صورتگر (نفر دوم)، محمدباقر منهاج (نفر سوم)