A comparative experimental evaluation on performance of type-1 and interval type-2 Takagi-Sugeno fuzzy models

2021 ◽  
Author(s):  
Kehua Yuan ◽  
Wentao Li ◽  
Weihua Xu ◽  
Tao Zhan ◽  
Libo Zhang ◽  
...  
Keyword(s):  
Experimental Evaluation ◽  
Fuzzy Models ◽  
Interval Type ◽  
Takagi Sugeno

scholarly journals Type-1 and Type-2 effective Takagi-Sugeno fuzzy models for decentralized control of multi-input-multi-output processes

2017 ◽  
Vol 52 ◽  
pp. 26-44 ◽  
Author(s):  
Qian-Fang Liao ◽  
Da Sun ◽  
Wen-Jian Cai ◽  
Shao-Yuan Li ◽  
You-Yi Wang
Keyword(s):  
Decentralized Control ◽  
Fuzzy Models ◽  
Takagi Sugeno

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

2018 ◽  
Vol 40 (6) ◽  
pp. 2011-2023 ◽  
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.

An interval type-2 hesitant fuzzy best-worst method

2021 ◽  
pp. 1-28
Author(s):  
Ashraf Norouzi ◽  
Hossein Razavi hajiagha
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Uncertain Data ◽  
Interval Type ◽  
Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

scholarly journals Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Actuator Saturation ◽  
Design Method ◽  
Fuzzy Model ◽  
The Stability ◽  
Interval Type ◽  
Takagi Sugeno

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno

scholarly journals Design of Type-1 and Interval Type-2 Fuzzy PID Control for Anesthesia Using Genetic Algorithms

2014 ◽  
Vol 06 (02) ◽  
pp. 70-93 ◽  
Author(s):  
Hugo Araujo ◽  
Bo Xiao ◽  
Chuang Liu ◽  
Yanbin Zhao ◽  
H. K. Lam
Keyword(s):  
Genetic Algorithms ◽  
Pid Control ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Interval Type
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