计算机工程与应用2017，Vol.53Issue(14)：45-50,6.DOI:10.3778/j.issn.1002-8331.1512-0001
改进并行粒子群算法优化RBF神经网络建模
RBF neural network for modeling based on improved parallel parti-cle swarm optimization
摘要
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 decreas-ing inertia weight to speed up the convergence of particles. Finally, the improved algorithm is used to optimize the parame-ters 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.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Key words
parallel particle swarm optimization/adaptive mutation operation/Radial Basis Function(RBF)neural net-work/average fitness/power amplifier modeling分类
信息技术与安全科学引用本文复制引用
陆亚男,南敬昌,高明明..改进并行粒子群算法优化RBF神经网络建模[J].计算机工程与应用,2017,53(14):45-50,6.基金项目
国家自然科学基金(No.61372058) （No.61372058）
辽宁省高校优秀科技人才支持计划(No.LR2013012) （No.LR2013012）
辽宁省教育厅科学研究一般项目(No.L2015209) （No.L2015209）
横向基金(No.14-2097-1). （No.14-2097-1）
