A Novel BA Algorithm for MIMO Hammerstein Model Identification under Heavy-Tailed Noise

Hammerstein model can finely express nonlinear characteristics of a practical process. But there is no general analytical method for the identification of Multiple-Input and Multiple-Output (MIMO) Hammerstein model, especially under cases with heavy tail noise. Meanwhile BA algorithm, as a novel heu...

Full description

Saved in:
Bibliographic Details
Published in2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) Vol. 2; pp. 289 - 293
Main Authors Jin, Qibing, Lu, Delong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2018
Subjects
Convergence
heavy tail noise
Heuristic algorithms
MIMO communication
OCBA algorithm
Optimization
Sociology
Statistics
system identification
Online AccessGet full text

Cover

More Information
Summary:Hammerstein model can finely express nonlinear characteristics of a practical process. But there is no general analytical method for the identification of Multiple-Input and Multiple-Output (MIMO) Hammerstein model, especially under cases with heavy tail noise. Meanwhile BA algorithm, as a novel heuristic optimization method, has a strong computing power to solve nonlinear complex problems. In this paper, firstly we propose an Opposite-based learning mean and Cauchy mutation BA algorithm (OCBA) to strengthen global and local exploration ability of standard BA algorithm. Then we transform the identification problem into an optimization problem and apply the proposed OCBA. Numerical simulations are carried out to verify the superiority of the OCBA for MIMO Hammerstein model identification.
DOI:10.1109/IHMSC.2018.10172