一种免疫算法与SVR的Hilbert-Huang边界优化

Hilbert-Huang Boundary Optimization of Immune Algorithm and SVR

Hilbert-Huang变换(Hilbert-Huang transform,HHT)在对信号进行经验模态分解(Empirical modedecomposition,EMD)和对各内禀模态函数(Intrinsic mode function,IMF)进行Hilbert变换时都会出现边界问题。为了克服该问题,本文提出了基于离散均匀免疫算法(Discrete uniform immune algorithm,DUIA)和支持向量回归(Support vector regression,SVR)的HHT边界优化方法。该方法采用DUIA优化SVR的参数,并利用SVR对数据延拓,以有效分析HHT边界问题。通过对正弦叠加信号和实际信号的仿真分析表明:所提出的算法可有效解决HHT中存在的边界问题,且其效果优于SVR的数据延拓方法。

The Hilbert-Huang transform （HHT） boundary problem appears when the signal is decomposed by the empirical mode decomposition （EMD） method as well as the intrinsic mode function （IMF） in Hilbert transform. Therefore, the HHT boundary optimization method based on discrete uniform immune algorithm （DUIA） and support vector regression （SVR） is proposed to overcome the problem. To effectively analyze the boundary problem of HHT, the scheme can use DUIA to optimize parameters of SVR, and then predict the signal by the trained optimal SVR model. For the sine superposition and practical signals, the corresponding simulation results demonstrate that the proposed algorithm can effectively solve the boundary problem of HHT, and its performance is better than the prediction method by SVR.