Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211006635 ◽

2021 ◽

pp. 014233122110066

Author(s):

Yang Chen ◽

Jiaxiu Yang

Keyword(s):

Fuzzy Logic ◽

Back Propagation ◽

Membership Functions ◽

Identification Problems ◽

Fuzzy Logic Systems ◽

Fuzzy Identification ◽

Logic Systems ◽

Academic Topic ◽

Interval Type

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

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Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods

Information Sciences ◽

10.1016/j.ins.2008.08.008 ◽

2009 ◽

Vol 179 (13) ◽

pp. 2146-2157 ◽

Cited By ~ 40

Author(s):

Gerardo M. Méndez ◽

M. de los Angeles Hernandez

Keyword(s):

Fuzzy Logic ◽

Least Squares ◽

Back Propagation ◽

Hybrid Learning ◽

Fuzzy Logic Systems ◽

Orthogonal Least Squares ◽

Logic Systems ◽

Propagation Methods ◽

Interval Type

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Study on permanent magnetic drive forecasting by designing Takagi Sugeno Kang type interval type-2 fuzzy logic systems

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217694682 ◽

2018 ◽

Vol 40 (6) ◽

pp. 2011-2023 ◽

Cited By ~ 8

Author(s):

Dazhi Wang ◽

Yang Chen

Keyword(s):

Fuzzy Logic ◽

Back Propagation ◽

Operating Conditions ◽

Fuzzy Logic Systems ◽

Logic Systems ◽

Interval Type ◽

Takagi Sugeno ◽

Magnetic Drive

The process of permanent magnetic drive (PMD) presents high uncertainty under the complex operating conditions. In this paper, a type of Takagi Sugeno Kang (TSK) interval type-2 fuzzy logic systems (IT2 FLSs) under the Karnik-Mendel (KM) structure is designed for data-based PMD torque and revolutions per minute (rpm) forecasting. For designing the antecedent and input measurement of TSK IT2 FLSs, the primary membership functions (MFs) of interval type-2 fuzzy sets (IT2 FSs) are all selected as Gaussian type-2 MFs with uncertain derivation, while the consequent parameters are chosen as type-1 fuzzy numbers. According to matrix transformation, the complicated task of calculating derivatives in the TSK IT2 FLSs under the Karnik-Mendel structure can be managed subtly by some elementary vectors and partitioned matrices. And the parameters of the proposed systems are also tuned by the back propagation (BP) algorithms. Simulation examples based on the data of PMD torque and rpm are used to test the advanced fuzzy logic systems forecasting methods. The effective and feasibility of forecasting by the proposed type-2 systems compared with their type-1 counterparts is illustrated in the light of Monte Carlo simulations, convergence and stability analysis.

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