Python VMD
时间: 2023-12-05 08:39:26 浏览: 34
根据提供的引用内容,可以了解到VMD是一种用于信号处理的算法,可以很好地解决EMD中出现的模态混叠现象。下面提供一个Python VMD的实现方法:
```python
import numpy as np
from scipy.signal import hilbert
def VMD(signal, alpha, tau, K, DC):
"""
:param signal: 待分解信号
:param alpha: 正则化参数
:param tau: 阈值参数
:param K: 分解模态数
:param DC: 是否分解直流分量
:return: 分解后的模态函数和残差
"""
N = signal.shape[0]
signal = signal - np.mean(signal)
if DC:
signal = signal - np.mean(signal)
else:
signal = signal - np.mean(signal) * np.ones((N,))
# 初始化
omega = np.zeros((N, K))
u = np.zeros((N, K))
u_hat = np.zeros((N, K))
u_hat_sum = np.zeros((N,))
omega_hat = np.zeros((N, K))
omega_hat_sum = np.zeros((N,))
alpha_k = alpha * np.ones((K,))
lambda_k = np.zeros((K,))
h = np.hamming(N)
h = h / np.sum(h)
# 迭代分解
for k in range(K):
u[:, k] = signal - np.sum(omega, axis=1)
for i in range(1, 100):
u_hat[:, k] = u[:, k] + hilbert(omega[:, k])
omega_hat[:, k] = np.real(hilbert(u_hat[:, k])) - u_hat[:, k]
lambda_k[k] = np.sum(np.abs(omega_hat[:, k]))
if lambda_k[k] < tau:
break
omega[:, k] = alpha_k[k] * np.real(hilbert(omega_hat[:, k])) + omega[:, k]
u_hat_sum = u_hat_sum + u_hat[:, k]
omega_hat_sum = omega_hat_sum + omega_hat[:, k]
# 计算残差
r = signal - u_hat_sum - np.sum(omega_hat, axis=1)
return u_hat, r
```
其中,signal为待分解信号,alpha为正则化参数,tau为阈值参数,K为分解模态数,DC为是否分解直流分量。函数返回分解后的模态函数和残差。