摘要
聚合经验模态分解(ensemble empirical mode decomposition,EEMD)方法极好地抑制了EMD算法的模态混叠,但仍未很好地解决端点效应,另外由于EEMD加入白噪声的幅值系数及总体平均次数需靠经验设定,不利于信号快速、准确地分解与重构.针对上述问题,提出了自适应KEEMD(KELM-EEMD)方法.首先,基于核函数的极限学习机结合镜像法进行极值点延拓以抑制端点效应,并用于仿真信号分解及小麦反射光谱的去噪,验证了该方法抑制端点效应的有效性.其次,通过抑制端点效应后分解获得的高频分量,自适应地确定算法所需加入白噪声的幅值系数及总体平均次数,将此自适应KEEMD方法用于油菜、菠菜反射光谱的去噪.结果表明:该方法更加快速地获得了与靠经验设定参数的KEEMD方法基本一致的去噪效果.
Ensemble empirical mode decomposition ( EEMD ) overcomes the mode mixing problem of EMD, however the end effect still exists. Additionally, two key parameters in EEMD: the amplitude of added noise and the number of ensemble times are set up by experience, not conducive to complete the signal decomposition fast and accurately. To solve the above problems, a method of adaptive KEEMD was proposed in this paper. First, the end effect was restraimed through an extrema extension based on the kernel extreme learning machine ( KELM ) and mirror extension, then it was applied to the simulate signal decomposition and wheat reflectance spectrum denoising, and the effectiveness of restraining end effect was verified by the result. Second, through the high frequency component of signal obtained by above step, the two adaptive parameters in KEEMD was obtained. The adaptive method was used to cole and spinach reflectance spectrum denoising. Results show that denoising results can be obtained more quickly and be consistent with the non-adaptive method.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2016年第4期513-520,共8页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61374143)
关键词
聚合经验模态分解
端点效应
核函数极限学习机
自适应
光谱去噪
ensemble empirical mode decomposition(EEMD)
end effect
kernel extreme learning machine(KELM)
adaptive
spectrum denoising
