计算机工程与应用2017，Vol.53Issue(6)：135-140,6.DOI:10.3778/j.issn.1002-8331.1607-0066
基于混沌量子粒子群的FHN神经元UWB信号检测
Ultra-wideband signal detection based on chaotic quantum particle swarm optimization FHN model method
摘要
Abstract
For quantum particle swarm FHN neuron model reduces the particle swarm diversity, easy to fall into local optimum, leads to a lower accuracy in UWB-IR signal detection, chaos optimization algorithm is introduced to quantum update parameter in quantum particle swarm optimization. A UWB-IR signal detection method based on chaotic quantum particle swarm optimization is proposed for FHN neurons model. The convergence of the proposed algorithm is analyzed. The performance of the proposed algorithm is simulated. Simulation results show that the proposed algorithm is able to improve the diversity of particle swarm and the convergence rate and the accuracy, attain the optimal system parameter simultaneously. The UWB-IR signal is adaptively detected under different noise intensities.关键词
超宽带/信号检测/FHN神经元模型/混沌量子粒子群Key words
ultra-wideband/signal detection/FHN neurons model/chaotic quantum particle swarm optimization分类
信息技术与安全科学引用本文复制引用
陈博文,蒋磊,刘潇文,张群..基于混沌量子粒子群的FHN神经元UWB信号检测[J].计算机工程与应用,2017,53(6):135-140,6.基金项目
国家自然科学基金(No.61471386). （No.61471386）
