Nonlinear dynamical system identification based on evolutionary interval type-2 TSK fuzzy systems

Author

Zhang Jianhua ; Chen Hongjie ; Wang Rubin

Author_Institution

Sch. of Inf. Sci. & Eng., East China Univ. of Sci. & Technol., Shanghai, China

fYear

2015

fDate

23-25 May 2015

Firstpage

2871

Lastpage

2876

Abstract

For an interval type-2 fuzzy logic system (IT2FLS), its structure and parameters are learned simultaneously by using evolutionary strategy in this paper. Then gradient descent (GD) and recursive least-squares with forgetting factor (FFRLS) algorithms are employed to optimize the parameters of the IT2FLS. Furthermore, a more efficient type-reduction method, called enhanced iterative algorithm with stop condition (EIASC), is utilized. Finally, an evolutionary interval type-2 TSK fuzzy logic system (EIT2FLS) is developed. The results of applying EIT2FLS to nonlinear systems identification problems demonstrated the superiority of the developed EIT2FLS to existing methods.

Keywords

evolutionary computation; fuzzy logic; fuzzy systems; identification; iterative methods; nonlinear dynamical systems; enhanced iterative algorithm; evolutionary interval type-2 TSK fuzzy logic system; evolutionary interval type-2 TSK fuzzy systems; evolutionary strategy; forgetting factor algorithms; gradient descent; interval type-2 fuzzy logic system; nonlinear dynamical system identification; nonlinear systems identification problems; recursive least-squares; stop condition; type-reduction method; Algorithm design and analysis; Fuzzy logic; Fuzzy systems; Nonlinear systems; Testing; Training; Training data; EIASC; Evolutionary strategy; Hybrid learning; IT2FLS; Nonlinear systems identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2015 27th Chinese

Conference_Location

Qingdao

Print_ISBN

978-1-4799-7016-2

Type

conf

DOI

10.1109/CCDC.2015.7162416

Filename

7162416