NDLI: Study of RF power amplifier behavior models based on BP improved algorithm

Content Provider	IEEE Xplore Digital Library
Author	Jingchang Nan Jianwei Ren Mifang Cong Luhong Mao
Copyright Year	2011
Description	Author affiliation: School of electrics and information engineering, Liaoning Technical University, 125105, Huludao, Liaoning, China (Jingchang Nan; Jianwei Ren; Mifang Cong) \|\| School of electrics and information engineering, Tianjin University, 300072, Tianjin, China (Luhong Mao)
Abstract	How to model the power amplifier behavior accurately is the key to system-level simulation. BP neural network can be used to simulate random nonlinear system, but it easily falls into the local minimum points and has no enough precision. So, this article proposes two improved model based on BP neural network model, one is cascading model BP-RBF, and the other is PSO_BP neural network. Design amplifier circuit in ADS2009 utilizing the freescale semiconductor chip MRF6S21140, and then extract voltage data as the simulation data. Carry on the MATLAB fitting simulation by BP, BP-RBF as well as PSO_BP, compared with voltage RMS error (RMSE), epochs and convergence time. Eventually, the results show that the improved algorithm BP-RBF, PSO_BP models have better fitting function than BP model, and fit the characteristics of power amplifier accurately, then have the important application value to construct system simulation.
Starting Page	376
Ending Page	379
File Size	195783
Page Count	4
File Format	PDF
ISBN	9781424482658
e-ISBN	9781424482689
DOI	10.1109/MAPE.2011.6156275
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radio frequency PSOBP neural network power amplifier Fitting simulation BP neural network Fitting Power amplifiers Data models Numerical models Integrated circuit modeling BP-RBF neural network Biological neural networks
Content Type	Text
Resource Type	Article

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