【摘要】 根据带噪EP信号的α稳定分布特性和分数低阶矩理论 ,本文分析了DLMS自适应算法在低阶α稳定分布噪声条件下退化的原因 ,并从理论上研究了由本文部分作者提出的DLMP诱发电位潜伏期变化自适应估计算法在高斯和非高斯环境下的韧性及其保持韧性的原因。理论分析和计算机模拟以及实验数据分析的结果表明 ,α稳定分布噪声模型是一种适合于描述带噪EP信号统计特性的随机噪声模型 ,基于分数低阶矩的DLMP自适应算法在高斯和低阶α稳定分布噪声环境下均具有良好的韧性。用这种方法所检测估计的EP信号潜伏期变化 ,与神经系统的实际状态和变化一致 ,具有较高的可靠性。更多还原

【Abstract】 Based on the α-stable distribution theory and the properties of EP signals and noises, this paper analyzes the degeneration of the DLMS algorithm and the robustness of the DLMP algorithm proposed by part of the authors for latency change estimation. It was shown that the α-stable model fitted the noises found in the impact acceleration experiment under study better than the Gaussian model. The DLMP was more robust under both Gaussian and lower order α-stable noise conditions, and the results obtained were identical with the status and changes of the central nervous system.更多还原