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Power amplifier behavioral modeling method based on depth reconstruction model

A power amplifier, deep reconstruction technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of complex calculation, incomplete understanding of nonlinear distortion of power amplifiers, affecting the performance improvement of power amplifiers, etc.

Inactive Publication Date: 2016-01-06
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0005] The purpose of the present invention is to provide a power amplifier behavior modeling method based on a deep reconstruction model (DeepReconstructionModel, DRM). Incomplete understanding of linear distortion, which affects the performance improvement of power amplifiers

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  • Power amplifier behavioral modeling method based on depth reconstruction model
  • Power amplifier behavioral modeling method based on depth reconstruction model
  • Power amplifier behavioral modeling method based on depth reconstruction model

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Embodiment Construction

[0080] Hereinafter, the class D half-bridge power amplifier is taken as an example, and the embodiments of the present invention will be described in detail in conjunction with the accompanying drawings.

[0081] The MOS tube of the class D power amplifier works in the switch state, which is a typical nonlinear system. like figure 1 Shown is a class D half-bridge power amplifier circuit, in which the amplitude of the triangular wave signal is 10V and the frequency is 40kHz; the input dual-tone signal x is 436Hz and 3kHz respectively, and the amplitude is 4V. The pulse width modulation signal (PWM) is output through the class D amplifier, and then through the low-pass filter, the output signal is y d , with distortion.

[0082] like figure 2 Shown is the structure diagram of the DRM model, which consists of two parts: restricted Boltzmann machine and Elman neural network.

[0083] The present invention is a behavioral modeling method of a power amplifier based on a deep re...

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Abstract

The invention discloses a power amplifier behavioral modeling method based on a depth reconstruction model. The depth reconstruction model combines the advantages of a deep learning theory and an Elman neural network. A restricted Boltzmann machine is used to initialize the weight coefficient of the neural network. In a modeling process, the number of times of iteration is small, but faster convergence is acquired. Due to the fact that the output of the Elman neural network is related to immediate input and historical input, a power amplifier behavioral model is accurately reconstructed by describing the memory effect of a nonlinear system.

Description

technical field [0001] The invention relates to the application field of nonlinear system modeling and analysis, in particular to a neural network-based power amplifier behavior model modeling and analysis method. Background technique [0002] The power amplifier (Power Amplifier, PA) is a key part of the transmitter and one of the main sources of the characteristics of the radiation source. The power amplifier is a complex nonlinear system. The research on the system modeling method has important practical significance for the measurement of the nonlinear characteristics of the power amplifier, the parameter extraction of the behavior model, and the use of pre-distortion technology to eliminate the nonlinear distortion of the power amplifier. and application value. [0003] The modeling methods of power amplifiers can be divided into two types: physical modeling and behavioral modeling: physical modeling usually requires knowing the circuit structure of the power amplifier...

Claims

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Application Information

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IPC IPC(8): G06N3/02G06N3/08
CPCG06N3/08G06N3/047G06N3/048G06N3/044
Inventor 邵杰金相君杨恬甜安文威张鑫
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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