TY - JFULL AU - C. Pislaru and A. Shebani PY - 2014/10/ TI - Identification of Nonlinear Systems Using Radial Basis Function Neural Network T2 - International Journal of Mechanical and Mechatronics Engineering SP - 1677 EP - 1683 VL - 8 SN - 1307-6892 UR - https://publications.waset.org/pdf/9999894 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 93, 2014 N2 - This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function. ER -