Adaptive Kernel RBFNN Based on Normalized Least Mean Square Algorithm

被引：0

作者：

Huo Y. ^{[1
]}

Gong Q. ^{[1
]}

Qi Y. ^{[2
]}

An Y. ^{[1
]}

机构：

[1] College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou

[2] College of Computer Science and Engineering, Northwest Normal University, Lanzhou

来源：

Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2022年 / 45卷 / 02期

关键词：

Adaptive filtering; Nonlinear system identification; Normalized least mean square algorithm; Radial basis function neural network;

D O I：

10.13190/j.jbupt.2021-132

中图分类号：

学科分类号：

摘要：

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. © 2022, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.

引用

页码：29 / 35

页数：6

共 14 条

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