Type-2 fuzzy set based neuro-fuzzy model for identification and control of Nonlinear systems

被引：0
|
作者：
Singh, Madhusudan [1 ]
Hanmandlu, M. [2 ]
Srivastava, Smriti [1 ]
Gupta, J. R. P. [1 ]
机构：
[1] NSIT, Inst & Control Engn Dept, New Delhi, India
[2] IIT, Elect Engn Dept, Delhi, India
来源：
3RD INT CONF ON CYBERNETICS AND INFORMATION TECHNOLOGIES, SYSTEMS, AND APPLICAT/4TH INT CONF ON COMPUTING, COMMUNICATIONS AND CONTROL TECHNOLOGIES, VOL 2 | 2006年
关键词：
identification; control; neuro-fuzzy systems; Nonlinear systems and type-2 fuzzy sets;
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：
A novel method for calculating the system dynamics of a nonlinear system using Neuro-Fuzzy model based on type-2 fuzzy sets is proposed. The model can handle uncertainties in the rules arising out of type-2 fuzzy sets that bear variation in the membership functions. The approach involves the operations of fuzzification, inference and output processing for finding the number of rules whereas the defuzzification operation is done by neural network. The neuro-fuzzy Model derived using type-2 fuzzy sets is used for both identification and control of nonlinear systems very efficiently, It is demonstrated that type-2 fuzzy logic systems (FLS) are more effective over type-1 FLS for handling uncertainties.
引用
收藏
页码：5 / +
页数：3
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