基于蚁狮算法优化的BP-RBF 功放行为模型研究

为了准确描述射频功率放大器特性,在仿真过程中,建立一个良好的功放行为模型就变得极其关键。文中提出了一种基于蚁狮算法(Ant Lion Optimizer,ALO)优化的BP-RBF 级联神经网络射频功放行为模型,首先,采用飞思卡尔半导体芯片设计射频功放电路,对从设计的电路中提取出的电压数据进行处理,然后利用蚁狮种群中的多个个体并行寻优的能力,优化BP-RBF 神经网络的权值和阈值,对改进优化后的ALOBP-RBF 神经网络模型进行MATLAB 仿真,通过比较电压均方根误差验证模型精确性。仿真结果表明,相比于BP-RBF、GABP-RBF 模型,该模型具有更高的精度、更快的收敛速度,可以精确地模拟功率放大器的特性,对射频电路的建模具有重要意义。

Research on BP-RBF Power Amplifier Behavior Model Optimized by Ant Lion Algorithm

In order to describe the characteristics of RF power amplifier accurately, it is very important to establish a good behavior model in the simulation. In this paper, a kind of RF amplifier behavior of BP-RBF cascade neural network optimized by ant lion algorithm (Ant Lion Optimizer, ALO) is proposed. First, the RF power amplifier circuit is designed by Freescale semiconductor chip, and the voltage data extracted from the designed circuit is processed. Then, the weights and thresholds of the BP-RBF neural network are optimized by using the ability of several individuals in the population of the lion ant in parallel, and the improved optimized ALOBP-RBF neural network model is simulated by MATLAB. The accuracy of the model is verified by comparing the voltage root mean square error. The simulation results show that, compared with the BP-RBF and GABP-RBF models, the model has higher accuracy and faster convergence speed, and can accurately simulate the characteristics of the power amplifier, which is of great significance for the modeling of RF circuits.