%0 Journal Article
%A AN Yaqi
%A GONG Qi
%A QI Yongfeng
%A HUO Yuanlian
%T Adaptive Kernel RBFNN Based on Normalized Least Mean Square Algorithm
%D 
%R 10.13190/j.jbupt.2021-132
%J Journal of Beijing University of Posts and Telecommunications
%P 29-35
%V 45
%N 2
%X To make the adaptive kernel radial basis function neural network (RBFNN) exhibit the characteristics of fast convergence and steady-state error, a method that optimizes the adaptive kernel RBFNN by using the normalized least mean square as the learning algorithm is proposed. Based on the gradient descent algorithm, we derive the normalized least mean square (NLMS) algorithm with a variable step factor, and use it as a learning algorithm to update the weights and the biases of the adaptive kernel RBFNN. The simulation results in nonlinear system identification and pattern classification show that using NLMS learning algorithm to train adaptive kernel RBFNN has faster convergence speed and relatively less steady-state error compared with other learning algorithms.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-132