北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (6): 121-0.

• 论文 • 上一篇    

基于RBFNN的改进变步长最小均方自适应滤波算法

火元莲,巩琪,安娅琦,丁瑞博   

  1. 西北师范大学
  • 收稿日期:2022-09-28 修回日期:2023-01-02 出版日期:2023-12-28 发布日期:2023-12-29
  • 通讯作者: 火元莲 E-mail:hylqqq@ nwnu. edu. cn

An Improved Variable Step Size Least Mean Square Adaptive Filtering Algorithm Based on RBFNN

  • Received:2022-09-28 Revised:2023-01-02 Online:2023-12-28 Published:2023-12-29

摘要: 为了进一步提高径向基函数神经网络非线性自适应滤波的收敛性和稳态性,提出了一种基于径向基函数神经网络(RBFNN)的改进变步长最小均方自适应滤波算法。首先,利用反双曲正切函数的变步长最小均方算法和变尺度函数替换变步长模型中的定参数,以解决固定步长的弊端和变步长模型中定参数的选择问题;然后,将改进算法应用于RBFNN,训练更新网络的中心、宽度参数和输出权值参数,以提高梯度算法下RBFNN滤波的性能;最后,在非线性系统辨识及混沌时间序列预测中进行仿真实验,结果表明,所改进的算法在收敛速度和稳态误差性能上具有明显的优势。

关键词: 自适应滤波, 径向基函数神经网络, 变步长最小均方算法

Abstract: In order to further improve the convergence and stability of Radial basis function neural network nonlinear adaptive filtering, an improved variable step size least mean square adaptive filtering algorithm based on Radial basis function neural network (RBFNN) was proposed. On the basis of the variable step size least mean square algorithm and the inverse hyperbolic tangent function, a variable scale function is used to replace the fixed parameters in the variable step size model in order to solve the drawbacks of fixed step size in the algorithm and the problem of selecting fixed parameters in the variable step size model. Then, the improved algorithm is applied to RBFNN to update and train the center, width, and output weight parameters of the network, in order to improve the performance of RBFNN filtering under gradient algorithm. Finally, the simulation comparison experiments are carried out in nonlinear system identification and chaotic time series prediction. The results show that the proposed algorithm has obvious advantages in terms of convergence rate and steady-state error performance.

Key words: adaptive filtering, radial basis function neural network, variable step size least mean square algorithm

中图分类号: