关键词: 模糊小波神经网络, 小波函数, 自适应模糊推理系统, 改进粒子群优化算法, 记忆效应, 功放模型

Abstract:  As the influence of Power Amplifier（PA） nonlinear increases for Radio Frequency（RF） front-end, PA modeling has been more and more important. An adaptive fuzzy wavelet neural network is proposed, using improved simplified particle swarm optimization algorithm to build PA model with memory effect. First, the wavelet function is combined with the rules of adaptive neural fuzzy inference system to build the new model. The improved particle swarm algorithm not only introduces the worst position influence factor but also simplifies for neglecting the velocity of particle. The inertia weight is dynamic with the change of fitness function value. The new algorithm improves the convergence rate, avoids being trapped in local optimal solution. The simulation results show that this modeling approach can characterize PA feature effectively with small error and high precision.

Key words: fuzzy wavelet neural network, wavelet function, adaptive neural fuzzy inference system, simplified particle swarm optimization algorithm, memory effect, power amplifier model