@Article{iasc.2020.010125,
AUTHOR = {Tianhong Pan, Ying Song, Shan Chen},
TITLE = {Wiener Model Identification Using a Modified Brain Storm Optimization  Algorithm},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {5},
PAGES = {934--946},
URL = {http://www.techscience.com/iasc/v26n5/40814},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.010125}
}