%0 Journal Article
	%A Kyu Chul Lee and  Sung Hyun Yoo and  Choon Ki Ahn and  Myo Taeg Lim
	%D 2014
	%J International Journal of Electronics and Communication Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 92, 2014
	%T Function Approximation with Radial Basis Function Neural Networks via FIR Filter
	%U https://publications.waset.org/pdf/9999155
	%V 92
	%X 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.

	%P 1421 - 1424