Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 54, Issue -, Pages 26-38Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2016.04.006
Keywords
Recurrent RBFN; Type-2 Fuzzy sets; Self-evolving; System identification
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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 THRBFN perform better than the conventional RBFN. (C) 2016 Elsevier Ltd. All rights reserved.
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