Open Access
ARTICLE
Wiener Model Identification Using a Modified Brain Storm Optimization Algorithm
Tianhong Pan1,*, Ying Song2, Shan Chen2
1 Anhui Engineering Laboratory of Human-Robot Collabration System and Intelligent Equipment, School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, China
2 School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China
* Corresponding Author: Tianhong Pan. Email:
Intelligent Automation & Soft Computing 2020, 26(5), 934-946. https://doi.org/10.32604/iasc.2020.010125
Abstract
The Wiener model is widely used in industrial processes. It is
composed of a linear dynamic block and a nonlinear static block. Estimating the
Wiener model is challenging because of the diversity of static nonlinear
functions and the immeasurableness of intermediate signals owing to the series
structure of the Wiener model. Existing optimization algorithms cannot satisfy
the requirements of accuracy and efficiency of identification and often lose into a
local optimum. Herein, a modified Brain Storm Optimization (mBSO) is
proposed to estimate the parameters of the Wiener model. Many different
combinations of individuals from intra or extra-groups ensure the diversity of the
proposed mBSO algorithm. Furthermore, the mBSO algorithm incorporates a
multiplicative term. It is triggered by the current state of the population that
achieves a good balance between global exploration and local exploitation.
Comparative experiments are presented to demonstrate the effectiveness and
efficiency of the proposed method.
Keywords
Cite This Article
T. Pan, Y. Song and S. Chen, "Wiener model identification using a modified brain storm optimization algorithm,"
Intelligent Automation & Soft Computing, vol. 26, no.5, pp. 934–946, 2020.
Citations