@article{(Open Science Index):https://publications.waset.org/pdf/9999155, title = {Function Approximation with Radial Basis Function Neural Networks via FIR Filter}, author = {Kyu Chul Lee and Sung Hyun Yoo and Choon Ki Ahn and Myo Taeg Lim}, country = {}, institution = {}, abstract = {Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based learning algorithm is proposed to train radial basis function neural networks (RBFN) for nonlinear function approximation. Compared to the EKF training method, the proposed FIR filter training method is more robust to those environmental conditions. Furthermore , the number of centers will be considered since it affects the performance of approximation. }, journal = {International Journal of Electronics and Communication Engineering}, volume = {8}, number = {8}, year = {2014}, pages = {1421 - 1424}, ee = {https://publications.waset.org/pdf/9999155}, url = {https://publications.waset.org/vol/92}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 92, 2014}, }