Nonlinear Dynamical System Identification Based on Evolutionary Interval Type-2 TSK Fuzzy Systems

被引：0
|
作者：
Zhang Jianhua [1 ]
Chen Hongjie [1 ]
Wang Rubin [2 ]
机构：
[1] E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China
[2] E China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China
来源：
2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2015年
关键词：
IT2FLS; Evolutionary strategy; Hybrid learning; EIASC; Nonlinear systems identification; LOGIC SYSTEMS; NETWORK;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：2900 / 2905
页数：6
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