基于改进型径向基函数网络的功放非线性建模

针对功率放大器(PA)的非线性建模，提出了改进型径向基函数神经网络(RBFNN)模型。首先，在该模型的输入端加入延迟交叉项和输出反馈项，利用正交最小二乘法提取模型的权值以及隐含层的中心；然后，采用15MHz带宽的宽带码分多址（WCDMA）三载波信号对Doherty功放进行测试，其归一化均方误差(NMSE)可以达到-45dB；最后，通过逆F类功放对模型的普遍适用性进行验证。仿真结果表明，该模型能够更加真实地拟合功率放大器的特性。

Nonlinear modeling of power amplifier based on improved radial basis function networks

Aiming at the nonlinear modeling of Power Amplifier (PA), an improved Radial Basis Function Neural Networks (RBFNN) model was proposed. Firstly, time-delay of cross terms and output feedback were added in the input. Parameters (weigths and centers) of the proposed model were extracted using the Orthogonal Least Square (OLS) algorithm. Then Doherty PA was trained and validated successfully by 15MHz three-carrier Wideband Code Division Multiple Access (WCDMA) signal, and the Normalized Mean Square Error (NMSE) can reach -45dB. Finally, the inverse class F power amplifier was used to test the universality of the model. The simulation results show that the model can more truly fit characteristics of power amplifier.