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电子学报  2014 

基于解空间收缩的差分进化算法的CLAD序列优化

DOI: 10.3969/j.issn.0372-2112.2014.08.017, PP. 1571-1576

Keywords: 差分进化算法,去卷积,听觉诱发电位

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Abstract:

连续循环平均去卷积(ContinuousLoopAveragingDeconvolution,CLAD)方法是近年来提出的高刺激率条件下提取听觉诱发电位(AuditoryEvokedPotential,AEP)的有效方法.该方法对刺激序列的频域特性提出限制,给刺激序列的生成带来挑战.本文在差分进化(DifferentialEvolution,DE)算法的基础上,提出一种解空间收缩的差分进化(solution-spacecontractionDE,scDE)算法;该算法将刺激序列的频域约束和抖动量融合成一个单目标优化函数.根据抖动量的变化范围,提出新的变异算子,在维持种群多样性的同时保证搜索空间动态缩减从而生成有序性的最优刺激序列.该方法可以自动地生成各种参数下的低抖动率刺激序列,和传统随机生成序列人工筛选方式相比在保证噪声抑制能力的同时工作效率大大提高且抖动率更小.

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