辽宁工程技术大学学报（自然科学版）Issue(5)：656-659,4.DOI:10.3969/j.issn.1008-0562.2013.05.018
粒子群优化的RBF神经网络功放行为模型
Behavior models based on the PSORBF Algorithm for power amplifier
摘要
Abstract
Study the power amplifier nonlinear behavior model. Power amplifier presents nonlinear and memory effect. In order to have accurate power nonlinear amplifier behavior model, this study proposes the RBF neural network power amplifier behavior model based on the particle swarm optimization. It derives the input and output data from the RF power amplifier, by using free scale semiconductor device model and MRF6S21140 transistor circuits in the ADS. The results of simulation conclude the amplitude of the output voltage fitting curve and error curve. The results of the simulation indicates that PSO-RBF RF power amplifier model can achieve good precision, and can descript the RF power amplifier nonlinear and memory effects very well. This study provides a reference method of RF power amplifier behavior model.关键词
功率放大器/PSO-RBF/行为模型/RBF神经网络/粒子群算法/仿真建模/ADS/非线性Key words
driver amplifier/PSO-RBF/behavior model/RBFNN/PSO algorithm/Simulation modeling/ADS/nonlinearity引用本文复制引用
高明明,南敬昌,黄丽娜,马众..粒子群优化的RBF神经网络功放行为模型[J].辽宁工程技术大学学报（自然科学版）,2013,(5):656-659,4.基金项目
国家自然科学基金资助项目（60971048）；辽宁省博士启动基金资助项目（20091033）；中国煤炭工业协会2011年度科学技术研究指导性计划基金资助项目（MTKJ2011-339）；辽宁工程技术大学基金资助项目（2011ZR050）；辽宁省大学生创新创业训练计划资助项目 （）
