Correlation analysis method based SISO neuro-fuzzy Wiener model. (October 2017) - Jia, Li; Xiong, Qi; Li, Feng
Highlights: A special test signal that contains independent separable signals and random signal is adopted to identify the Wiener model, resulting in the identification problem of the linear model separated from that of the nonlinear part. The correlation analysis method and least square method can be employed for the identification of the linear part and nonlinear part, respectively. It can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of Wiener model. Abstract: A novel identification algorithm for neuro-fuzzy based single-input-single-output (SISO) Wiener model with colored noises is presented in this paper. The separable signal is adopted to identify the Wiener model, leading to the identification problem of the linear part separated from nonlinear counterpart. Then, the correlation analysis method can be employed for identification of linear part. Moreover, in the presence of random signal, the least square method based parameters estimation algorithm of static nonlinear part are proposed to avoid the impact of colored noise. As a result, proposed method can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of Wiener model. Examples are used to verify the effectiveness of the proposed method.