多变量Hammerstein-Wiener模型的参数辨识

Parameter Identification of Multivariate Hammerstein-Wiener Model

为了突破现存Hammerstein-Wiener模型参数辨识方法中假设输出非线性块可逆的限定条件,基于可分非线性最小二乘算法,提出由多个单变量Hammerstein子模型和一个多变量输出非线性块组成的多变量Hammerstein-Wiener模型的参数辨识方法.首先,以输出误差最小为准则使用Levenberg-Marquardt法辨识出输出非线性块和Hammerstein子模型的两个参数集.其次,对Hammerstein子模型使用基于张量积的奇异值分解,辨识出输入非线性块与中间线性块的参数.再次,理论分析了所提辨识方法的辨识收敛性.最后,通过仿真验证此法的有效性.

In order to break the limited condition that the output nonlinear blocks are reversible in existing Hammerstein-Wiener model parameter identification methods, a new parameter identification method of multivariate Hammerstein-Wiener model was proposed based on separable nonlinear least square algorithm. The model was comprised of multiple univariate Hammerstein submodels and one multivariate nonlinear block. First, two parameter sets were identified for output nonlinear block and Hammerstein submodels using Levenberg-Marquardt algorithm under the minimum output error criterion. Second, parameters of input nonlinear block and middle linear block were identified by singular value decomposition （SVD） of tensor product from Hammerstein submodels. Then, the identification convergence was theoretically analyzed. Finally,simulation results showed the effectiveness of the proposed method.