计算机工程与应用2017，Vol.53Issue(3)：120-123,182,5.DOI:10.3778/j.issn.1002-8331.1505-0057
基于改进粒子群算法的模糊小波神经网络建模
Fuzzy wavelet neural network for modeling based on improved particle swarm optimi-zation algorithm
摘要
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.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Key words
fuzzy wavelet neural network/wavelet function/adaptive neural fuzzy inference system/simplified particle swarm optimization algorithm/memory effect/power amplifier model分类
信息技术与安全科学引用本文复制引用
南敬昌,田娜..基于改进粒子群算法的模糊小波神经网络建模[J].计算机工程与应用,2017,53(3):120-123,182,5.基金项目
国家自然科学基金(No.61372058) （No.61372058）
辽宁省高等学校优秀科技人才支持计划(No.LR2013012) （No.LR2013012）
辽宁工程技术大学研究生科研资助项目(No.5B2014032). （No.5B2014032）
