Nonlinear system identification using Takagi-Sugeno type neuro-fuzzy model

2004 ◽  
Author(s):  
P.C. Panchariya ◽  
A.K. Palit ◽  
D. Popovic ◽  
A.L. Sharrna
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Fuzzy Model ◽  
Neuro Fuzzy ◽  
Takagi Sugeno

scholarly journals Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Mourad Turki ◽  
Anis Sakly
Keyword(s):  
Nonlinear System ◽  
Search Algorithm ◽  
Nonlinear System Identification ◽  
Fuzzy Model ◽  
Cuckoo Search ◽  
Identification Problem ◽  
Cuckoo Search Algorithm ◽  
Modelling Problem ◽  
Plant Modelling ◽  
Takagi Sugeno

A new method called cuckoo search (CS) is used to extract and learn the Takagi–Sugeno (T–S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T–S fuzzy model. These parameters are learned simultaneously. The optimized T–S fuzzy model is validated by using three examples: the first a nonlinear plant modelling problem, the second a Box–Jenkins nonlinear system identification problem, and the third identification of nonlinear system, comparing the obtained results with other existing results of other methods. The proposed CS method gives an optimal T–S fuzzy model with fewer numbers of rules.

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