关键词: 并行粒子群算法, 自适应变异操作, 径向基函数（RBF）神经网络, 平均适应度, 功放建模

Abstract: Aiming at the problem that the modeling accuracy of neural network power amplifier is not high and easy to fall into local extremum, a new improved parallel particle swarm optimization algorithm （Improved Parallel Particle Swarm Optimization, IPPSO） is proposed. The adaptive mutation operation is introduced into the improved algorithm based on the parallel particle swarm algorithm, which avoids falling into local optimum. Meanwhile, the global optimal position of the population is added to the speed of the particles, and it adjusts learning factor adaptively and linear decreasing inertia weight to speed up the convergence of particles. Finally, the improved algorithm is used to optimize the parameters of RBF neural network, and the network is used to model the nonlinear power amplifier. Compared with the standard particle swarm algorithm, the root mean square error of this method is improved by 19.08%, which verifies the feasibility of the algorithm and improves the accuracy of the neural network power amplifier modeling effectively.

Key words: parallel particle swarm optimization, adaptive mutation operation, Radial Basis Function（RBF） neural network, average fitness, power amplifier modeling