MIMO hammerstein system identification using LS-SVM and steady state time response

• Published in: 2017 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 1 - 7
• Main Authors: Castro-Garcia, Ricardo, Agudelo, Oscar Mauricio, Suyken, Johan A. K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2017
• Subjects: Computational modeling; Estimation; Kernel; MIMO communication; Noise measurement; Steady-state; Support vector machines
• DOI: 10.1109/SSCI.2017.8280928

A new methodology for identifying Multiple Input Multiple Output (MIMO) Hammerstein Systems is presented in this paper. The method consists of two stages. In the first stage, a Least Squares Support Vector Machine (LS-SVM) is used to model the nonlinear block of the Hammerstein System from its steady-state response. In the second stage, the intermediate variables are computed by using the previously estimated nonlinear block. Then, the linear block is estimated from the latter and the known outputs by using subspace identification methods. The method is very flexible concerning the class of problems it can handle and no previous knowledge about the underlying non-linearities is required except for very mild assumptions. It is particularly effective when dealing with hard to model nonlinearities where other methods often fail. Also, it can handle different numbers of inputs/outputs and performs well in the presence of white Gaussian noise. The performance of the proposed methodology is evaluated through two simulation examples with different levels of noise. The results of this evaluation are compared with those of some state of the art techniques.