基于神经网络的接收机宽带非线性行为建模

doi:10.16180/j.cnki.issn1007-7820.2022.08.001

Abstract

Abstract:

In order to predict the nonlinear effect of receiver in complex electromagnetic environment, a nonlinear behavior model of receiver with memory effect is constructed based on real-value time-delay radial basis function neural network. The K-means clustering algorithm and the orthogonal least square method are respectively used to select and learn the center of the hidden layer and weight of the model, and the model is trained with the input and output measured data of the receiver. The model is verified by the in-phase and quadrature components of wideband signals. The simulation results are in good agreement with the measured data, and the normalized mean square errors of the model reaches -41.88 dB. The verification results show that the neural network model has fast convergence speed, good modeling accuracy and generalization ability.

Key words: receiver, nonlinear, behavioral modeling, radial basis function, neural network, memory effect, wideband signals, generalization ability

CLC Number:

TN85

LIU Guohua,LU Hongmin,CHEN Chongchong,LI Wanyu,WAN Jianpeng. Wideband Nonlinear Behavior Modeling of Receiver with Neural Network[J].Electronic Science and Technology, 2022, 35(8): 1-6.

Figures/Tables 9

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记忆深度		2	3	4	5
NMSE/dB	RVTDRBFNN	-35.96	-41.88	-40.45	-38.24
NMSE/dB	RVTDBPNN	-35.22	-34.57	-34.12	-35.36

Wideband Nonlinear Behavior Modeling of Receiver with Neural Network

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