摘要
利用移动偶极子模型提出了一种基于多源粒子群同步探索和随机迭代混合的脑磁源定位算法.然后利用混合算法对3,4,5个脑磁源进行仿真实验,并与标准粒子群和随机迭代算法作比较.实验表明了该混合算法既保持较低时间成本,且在精度和稳定性上具有较大的提高.
To study multiple MEG source localization using time sliced data,a new numerical scheme based on particle swarm optimization and stochastic iterative algorithm is presented.We validate the proposed algorithm against 3,4,5 sources and then compare its performance with those of particle swarm optimization and stochastic iterative algorithm.Finally,we discuss the implication of results and give the suggestion for further research.
出处
《数学的实践与认识》
北大核心
2015年第6期240-246,共7页
Mathematics in Practice and Theory
基金
福建省教育厅A类科技项目(JA12353)
福建师范大学福清分校科研项目(KY2012025)
关键词
粒子群算法
脑磁定位
反问题
particle swarm optimization
MEG sources localization
inverse problem
