Adaptive Kernel RBFNN Based on Normalized Least Mean Square Algorithm

doi:10.13190/j.jbupt.2021-132

Journal of Beijing University of Posts and Telecommunications ›› 2022, Vol. 45 ›› Issue (2): 29-35.doi: 10.13190/j.jbupt.2021-132

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Adaptive Kernel RBFNN Based on Normalized Least Mean Square Algorithm

HUO Yuanlian¹, GONG Qi¹, QI Yongfeng², AN Yaqi¹

1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China;
2. College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China

Received:2021-06-24 Published:2021-12-16

Abstract

Abstract: 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.

Key words: adaptive filtering, radial basis function neural network, normalized least mean square algorithm, nonlinear system identification

CLC Number:

TN713

HUO Yuanlian, GONG Qi, QI Yongfeng, AN Yaqi. Adaptive Kernel RBFNN Based on Normalized Least Mean Square Algorithm[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(2): 29-35.

References

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Adaptive Kernel RBFNN Based on Normalized Least Mean Square Algorithm

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