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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.


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.


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.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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