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author:

吴林煌 (吴林煌.) [1] | 苏凯雄 (苏凯雄.) [2] | 郭里婷 (郭里婷.) [3] | 吴子静 (吴子静.) [4]

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Abstract:

针对强记忆功放的非线性问题,提出一种基于自适应扩展卡尔曼滤波与神经网络的高功放(High power amplifier,HPA)预失真算法.采用实数固定延时神经网络(Real-valued focused time-delay neural network,RVFTDNN)对间接学习结构预失真系统中的预失真器和逆估计器进行建模,扩展卡尔曼滤波(Extended Kalman filter,EKF)算法训练神经网络,从理论上指出Levenberg-Marquardt(LM)算法是EKF算法的特殊情况,并用李亚普诺夫稳定性理论分析EKF算法的稳定收敛条件,推导出测量误差矩阵的自适应迭代公式.结果表明:自适应EKF算法的训练误差和泛化误差均比LM算法更低,预失真后的邻道功率比(Adjacent channel power ratio,ACPR)比LM算法改善了2 d B.

Keyword:

神经网络 自适应扩展卡尔曼滤波 非线性 预失真 高功率放大器

Community:

  • [ 1 ] 福州大学物理与信息工程学院,福州350116

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Source :

自动化学报

ISSN: 0254-4156

Year: 2016

Issue: 1

Volume: 42

Page: 122-130

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count: -1

30 Days PV: 4

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