Laser & Optoelectronics Progress, Volume. 57, Issue 1, 012001(2020)

Reverse Modeling Method for BRBP Neural Network Power Amplifier Based on Improved Ant Colony Algorithm

Jingchang Nan, Jing Zang*, and Mingming Gao
Author Affiliations
  • School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
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    Figures & Tables(9)
    BRBP neural network structure
    Overall circuit diagram of reconfigurable power amplifier
    Relationship between return loss S11 and capacitance C
    Relationship among power added efficiency PAE, output power P, and capacitance C
    Process of IACO-BRBP neural network reverse modeling for reconfigurable power amplifier
    Fitting contrast diagram of capacitance values C
    Fitting contrast diagram of output power P
    Comparison of output values C of three reverse models and actual values C required by ADS
    • Table 1. Comparison of performances of three modeling methods

      View table

      Table 1. Comparison of performances of three modeling methods

      Reverse modeling methodInput S11 combined CInput PAE combined CInput PAE combined P
      Meansquare errorRuntime /sMeansquare errorRuntime /sMeansquare errorRuntime /s
      IACO-BRBP0.00144.0252939.5779×10-44.0721770.00214.138958
      Adaptive η0.01554.2391080.00784.0483770.03194.235733
      Direct modeling8.76209.5854982.36823.1442560.33466.366971
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    Jingchang Nan, Jing Zang, Mingming Gao. Reverse Modeling Method for BRBP Neural Network Power Amplifier Based on Improved Ant Colony Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(1): 012001

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    Paper Information

    Category: Optics in Computing

    Received: Jun. 22, 2019

    Accepted: Jul. 22, 2019

    Published Online: Jan. 3, 2020

    The Author Email: Zang Jing (2681318835@qq.com)

    DOI:10.3788/LOP57.012001

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