Model Reduction of Discrete-Time Interval Type-2 T–S Fuzzy Systems

Zeng, Yi; Lam, Hak‐Keung; Wu, Ligang

doi:10.1109/tfuzz.2018.2836353

Cited by 18 publications

(6 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 33 ] The output processor incorporates defuzzifier and type‐reducer, it generates a type‐1 fuzzy system output or a crisp number. [ 34,35 ] For the classification purpose with type‐2 fuzzy, extracted features of power quality events based on FEM Z ,

\overset{W}{true}

, and κ matrices now these extracted features were tested for the optimization purpose. In the next step tested features of power signal disturbances trained for type‐2 fuzzy logic for classification.…”

Section: Classification Results and Discussionmentioning

confidence: 99%

A Novel Approach to Power Quality Analysis: Adaptive FEM Algorithm with Sparse Signal Decomposition

Rahul

2020

Advcd Theory and Sims

View full text Add to dashboard Cite

An adaptive approach of discretized triangular mesh based on finite element method hybrid with type‐2 fuzzy is utilized for detection and classification of power quality events. The concept of sparse signal decomposition is utilized with extraction of disturbances from power signals. An adaptive finite element method is then applied with the concept of stiffness matrix which extracts some relevant features of power quality events that act as input to type‐2 fuzzy classifiers. Finally performance comparison is carried out, the results of which show that the proposed technique has potential to analyze disturbances with high accuracy and minimum computational overhead.

show abstract

\overset{W}{true}

Section: Classification Results and Discussionmentioning

confidence: 99%

A Novel Approach to Power Quality Analysis: Adaptive FEM Algorithm with Sparse Signal Decomposition

Rahul

2020

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

“…Recently, several interval type-II T-S fuzzy controllers have been designed based on type-II fuzzy sets to address uncertainties in membership functions, and relevant works are presented in previous works. 134–136 Moreover, type-II fuzzy systems have also been combined with adaptive control methods to produce adaptive, intelligent, and robust controllers, for example, interval type-II fuzzy sliding-mode controllers, 137 backstepping type-II fuzzy controllers, 138 and robust H∞ type-II fuzzy controllers. 139 These studies have attracted unprecedented interest, because the designed controller can benefit from the advantages of both methods and overcome their inherent negative aspects.…”

Section: Resultsmentioning

confidence: 99%

A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

Underactuated systems represent a class of mechanical systems with fewer independent control actuators than degrees of freedom. The control problem of underactuated systems is one of the most challenging in the theory of nonlinear control because such systems exhibit strong coupling and highly nonlinear characteristics. Fuzzy systems have been successfully applied to control underactuated systems in both theoretical analysis and practical application. This survey describes the model and application of underactuated systems and classifies fuzzy systems in a straightforward manner. Moreover, this study analyzes problems facing conventional control methods and presents the development of fuzzy systems to solve these problems.

show abstract

“…The type-1 fuzzy model has been the mainstream model in the fuzzy control, but the lack of the ability to tackle uncertainties directly is a drawback [10] and [11]. Under the circumstance, more and more attention is paid to type-2 fuzzy model which can capture uncertainties directly by the type-2 fuzzy sets [12]. However, the general type-2 fuzzy sets will result in the complex design process and high computational expense.…”

Section: Introductionmentioning

confidence: 99%