Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks
Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks
复制标题
DOI：
10.1109/lmwc.2010.2052594
复制
发表时间：
2010-07
影响因子：
3
通讯作者：
Jianfeng Zhai;Jianyi Zhou;Lei Zhang;W. Hong
中科院分区：
文献类型：
--
作者：
Jianfeng Zhai;Jianyi Zhou;Lei Zhang;W. Hong
本文将动态模糊神经网络(D-FNN)应用于具有记忆效应的功率放大器(PA)的建模。D-FNN模型基于扩展径向基函数(RBF)神经网络实现Takagi-Sugeno-Kang(TSK)模糊系统。该模型采用在线自组织学习算法对模型参数进行训练，根据神经元对系统性能的重要性动态地对神经元进行招募或删除，避免了过拟合或过训练问题。D-FNN模型在我们的测试台上得到了验证，在该测试台上，Doherty PA被10 MHz和20 MHz的微波接入(WiMAX)全球互操作性信号所激励。实验结果表明，D-FNN模型能够准确地描述具有记忆效应的宽带功率放大器。
In this letter, dynamic fuzzy neural networks (D-FNN) are applied to model power amplifiers (PAs) with memory effects. The D-FNN model implements Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial bias function (RBF) neural networks. The parameters of the model are trained by the online self-organized learning algorithm, in which the neurons can be recruited or deleted dynamically according to their significance to system performance, and the over fitting or over training problems can be avoided. The D-FNN model is validated in our test bench in which a Doherty PA is excited with 10 MHz and 20 MHz worldwide interoperability for microwave access (WiMAX) signals. Experimental results show that the D-FNN model can give an accurate approximation to characterize the wideband PAs with memory effects.