Tianhong Pan1,*, Ying Song2, Shan Chen2
Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 934-946, 2020, DOI: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… More >
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