基于小波域数据的Hammerstein模型参数估计

摘要

针对非线性离散Hammerstein模型的输出存在随机噪声情况下,提出直接利用小波域的输入输出数据,估计出该模型的参数的方法.最小二乘法是时域参数估计的主要方法,随着对小波理论的深入研究,它在信号处理方面起着重要的作用.信号经过小波变换后,得到具有时频特征的小波域的信号,提高了信号的信噪比,去噪结果比时域和频域更有效.通过小波最小二乘法估计出模型的参数,与时域最小二乘法的估计参数相比较,仿真结果表明波域方法是可行的,有效的.%For the discrete nonlinear Hammerstein model with the noise corrupted output data, a method was proposed to estimate the parameters of the model with the input - output data in wavelet domain directly. The least squared ( LS) method is an important method for parameter estimation in time domain, with the developing of wavelet theory, it plays an important role in signal processing. By means of wavelet transform, the signal has both characteristics of time and frequency. It became a signal in wavelet domain, and increased the ratio of signal to noise. The de-noising result is more effective than in time domain and in frequency domain. The parameters of model were estimated by the wavelet least squared method. Compared with the least squared method in time domain, the proposed method is feasible and effective.