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Mathematics
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Advanced Methods in Fuzzy Control and Their Applications
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Advanced Methods in Fuzzy Control and Their Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 4931

Special Issue Editors

Prof. Dr. Cheung Chieh Ku

E-Mail Website
Guest Editor
Department of Marine Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 806, Taiwan
Interests: fuzzy control; lpv system; stochastic system; mixed performance control; marine engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chang-Hua Lien

E-Mail Website
Guest Editor
Department of Marine Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811, Taiwan
Interests: fuzzy control theory; neural networks; robust control; time-delay systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fuzzy theory presents a concept to characterize sets between 0 and 1 as human behavior. With the fuzzy set, numerous solutions for control issues, economic forecasting, bioinformatics, neuro-networks, signal communication, multi-systems, and computational intelligence have been widely proposed and investigated. Based on the advancement of mathematics, fuzzy theory is developing with a modeling approach, optimization algorithm, and computational ability and is furtherly applied for mechanical automation, artificial intelligence, image processing, risk assessment, and so on. As per recent vigorous results, fuzzy theory in an interesting topic of research for performing intelligent control of practical issues.

  • Control problem of novel fuzzy systems;
  • Development of fuzzy control technology;
  • Investigation of hybrid description for fuzzy systems;
  • Novel fuzzy control for practical applications;
  • Improvement of current fuzzy control.

Prof. Dr. Cheung Chieh Ku
Prof. Dr. Chang-Hua Lien
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy control theory
  • fuzzy sets and logic
  • fuzzy optimization method
  • fuzzy systems
  • fuzzy approximation
  • fuzzy decision

Published Papers (6 papers)

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Research

18 pages, 2187 KiB  
Article
State-Difference Feedback Control for Discrete-Time Takagi–Sugeno Fuzzy Descriptor Systems with Parameter Uncertainties and External Noises
by Zi-Yao Lin, Wen-Jer Chang and Che-Lun Su
Mathematics 2024, 12(5), 693; https://0-doi-org.brum.beds.ac.uk/10.3390/math12050693 - 27 Feb 2024
Viewed by 373
Abstract
This research focuses on the development of state-difference feedback controllers for discrete-time (DT) nonlinear descriptor systems. Discrete-time nonlinear DA systems consist of difference and algebraic equations and play a crucial role in describing dynamic behavior and capturing the constraints or relationships within the [...] Read more.
This research focuses on the development of state-difference feedback controllers for discrete-time (DT) nonlinear descriptor systems. Discrete-time nonlinear DA systems consist of difference and algebraic equations and play a crucial role in describing dynamic behavior and capturing the constraints or relationships within the system. However, analytical stability may pose additional challenges due to the unique characteristics of the system. Utilizing fuzzy model-based techniques, the DT nonlinear DA system discussed in this study can be effectively represented using the Takagi–Sugeno (T-S) fuzzy model. After linearizing the nonlinear system through the T-S fuzzy model, traditional linear control techniques become applicable. These techniques are then applied to T-S fuzzy systems to establish stability criteria. This article chooses the Lyapunov function as the method used to analyze system stability. Additionally, we use a free-weighting matrix to introduce additional degrees of freedom. In summary, this paper presents simulation results and discussions to verify the effectiveness of the proposed design approach. Full article
(This article belongs to the Special Issue Advanced Methods in Fuzzy Control and Their Applications)
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23 pages, 1718 KiB  
Article
A Fuzzy-Set Qualitative Comparative Analysis of Causal Configurations Influencing Mutual Fund Performance: The Role of Fund Manager Skill
by Pedro Carmona, Alexandre Momparler and Francisco Climent
Mathematics 2023, 11(21), 4500; https://0-doi-org.brum.beds.ac.uk/10.3390/math11214500 - 31 Oct 2023
Cited by 1 | Viewed by 1117
Abstract
A mutual fund is a common instrument for households and corporations to invest in the financial markets through diversified portfolios of securities. Investing in managed mutual funds involves relying on a fund manager’s knowledge, expertise, and investment strategy to beat the fund’s benchmark. [...] Read more.
A mutual fund is a common instrument for households and corporations to invest in the financial markets through diversified portfolios of securities. Investing in managed mutual funds involves relying on a fund manager’s knowledge, expertise, and investment strategy to beat the fund’s benchmark. The purpose of this paper is to help mutual fund investors in their fund selection process. The fuzzy-set qualitative comparative analysis (fsQCA) is the methodology applied to identify combinations of factors that facilitate the selection of performing mutual funds. The goal is to determine whether fund manager skill, as measured by Jensen’s Alpha and other qualitative factors, is a key driver of performance. Our research focuses on US-registered equity funds with a global investing scope over a 5-year period (2016–2021), and we combine three mutual fund databases to obtain more complete data while enhancing data accuracy and consistency. The findings reveal that both manager skill and fund size are pervasive factors included in all three successful combinations of sufficiency conditions leading to high-performance funds. In addition, it is verified that manager skill is the only necessary condition to ensure high returns on mutual funds. Investors’ fund selection process is a cumbersome task that can be simplified with the successful recipes provided by the fsQCA model. Full article
(This article belongs to the Special Issue Advanced Methods in Fuzzy Control and Their Applications)
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15 pages, 432 KiB  
Article
The Fuzzy Differential Transform Method for the Solution of the System of Fuzzy Integro-Differential Equations Arising in Biological Model
by Mitali Routaray, Prakash Kumar Sahu and Dimplekumar Navinchandra Chalishajar
Mathematics 2023, 11(18), 3840; https://0-doi-org.brum.beds.ac.uk/10.3390/math11183840 - 07 Sep 2023
Cited by 2 | Viewed by 680
Abstract
This article deals with the implementation of fuzzy differential transform method for solving a system of nonlinear fuzzy integro-differential equations. This system appears in a model of biological species living together. Though the differential transform method is an iterative method, the current approach [...] Read more.
This article deals with the implementation of fuzzy differential transform method for solving a system of nonlinear fuzzy integro-differential equations. This system appears in a model of biological species living together. Though the differential transform method is an iterative method, the current approach reduces this model to a set of nonlinear algebraic equations due to its delay terms. The basic definitions and theorems are first presented. The applicability and accuracy of the current methodologies have been demonstrated through the discussion of a few exemplary situations. Full article
(This article belongs to the Special Issue Advanced Methods in Fuzzy Control and Their Applications)
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24 pages, 456 KiB  
Article
Z-Number-Based Maximum Expected Linear Programming Model with Applications
by Meng Yuan, Biao Zeng, Jiayu Chen and Chenxu Wang
Mathematics 2023, 11(17), 3750; https://0-doi-org.brum.beds.ac.uk/10.3390/math11173750 - 31 Aug 2023
Viewed by 668
Abstract
In research of a better description for information uncertainty, Z-numbers, which are related to both the objective information and the subjective criticism, were first conceptualized by Zadeh. Because of its neologism, there have been multitudinous attempts toward continuation and expansion of the prototype. [...] Read more.
In research of a better description for information uncertainty, Z-numbers, which are related to both the objective information and the subjective criticism, were first conceptualized by Zadeh. Because of its neologism, there have been multitudinous attempts toward continuation and expansion of the prototype. In this paper, we mainly study varieties of theoretical preparations for classical Z-numbers and derive the maximum expected linear programming model of Z-numbers, which are constructed on the basis of reliability conversion factors and proliferation on applications due to their simplicity. Firstly, by means of transforming Z-numbers into LR fuzzy intervals through their reliability variable, the credibility distribution and inverse distribution of converted Z-numbers are stated precisely. Then, the operational law of independent variables and its expected value can be derived via credibility distribution. The maximum expected Z-number linear programming model is determined on the basis of previous theoretical preparations, and it transforms from a classical Z-number chance-constrained model into a crisp one. Finally, with the aim of improving the programming method, its application in pragmatic practice with the realistic examples of a supplier section and optimal portfolio problems are enumerated to interpret the effectiveness of our model. Full article
(This article belongs to the Special Issue Advanced Methods in Fuzzy Control and Their Applications)
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20 pages, 2630 KiB  
Article
Observer-Based Fuzzy Control of Uncertain Nonlinear Singular Systems under Multi-Performance Requirements
by Wen-Jer Chang, Yu-Min Huang and Yann-Horng Lin
Mathematics 2023, 11(12), 2632; https://0-doi-org.brum.beds.ac.uk/10.3390/math11122632 - 08 Jun 2023
Cited by 2 | Viewed by 807
Abstract
This paper discusses an observer-based fuzzy control problem for uncertain nonlinear singular systems under Multi-Performance Requirements (MPRs). The approach used in the paper is to model the system using a Takagi–Sugeno (T-S) fuzzy model that can be analyzed using linear control theories. The [...] Read more.
This paper discusses an observer-based fuzzy control problem for uncertain nonlinear singular systems under Multi-Performance Requirements (MPRs). The approach used in the paper is to model the system using a Takagi–Sugeno (T-S) fuzzy model that can be analyzed using linear control theories. The proposed control scheme is based on the Parallel Distributed Compensation (PDC) approach and Proportional Derivative (PD) control scheme. The goal is to design an observer-based fuzzy controller that achieves stability of the system and also satisfies the Guarantee Cost Control (GCC) constraint while maintaining a desired passive constraint. The stability analysis is performed using Lyapunov theory, and the sufficient conditions are transformed into a Linear Matrix Inequality (LMI) form using a Shur Complement, free-weighting matrix method and Singular Value Decomposition (SVD) techniques. The LMI conditions are then solved using convex optimization algorithms. Finally, the proposed control method is validated using a bio-economic system to demonstrate its effectiveness. Full article
(This article belongs to the Special Issue Advanced Methods in Fuzzy Control and Their Applications)
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18 pages, 2294 KiB  
Article
Passive Fuzzy Controller Design for the Parameter-Dependent Polynomial Fuzzy Model
by Cheung-Chieh Ku, Chein-Chung Sun, Shao-Hao Jian and Wen-Jer Chang
Mathematics 2023, 11(11), 2482; https://0-doi-org.brum.beds.ac.uk/10.3390/math11112482 - 28 May 2023
Viewed by 764
Abstract
This paper discusses a passive control issue for Nonlinear Time-Varying (NTV) systems subject to stability and attenuation performance. Based on the modeling approaches of Takagi-Sugeno (T-S) fuzzy model and Linear Parameter-Varying (LPV) model, a Parameter-Dependent Polynomial Fuzzy (PDPF) model is constructed to represent [...] Read more.
This paper discusses a passive control issue for Nonlinear Time-Varying (NTV) systems subject to stability and attenuation performance. Based on the modeling approaches of Takagi-Sugeno (T-S) fuzzy model and Linear Parameter-Varying (LPV) model, a Parameter-Dependent Polynomial Fuzzy (PDPF) model is constructed to represent NTV systems. According to the Parallel Distributed Compensation (PDC) concept, a parameter-dependent polynomial fuzzy controller is built to achieve robust stability and passivity of the PDPF model. Furthermore, the passive theory is applied to achieve performance, constraining the disturbance effect on the PDPF systems. To develop the stability criteria, by introducing a parameter-dependent polynomial Lyapunov function, one can derive some stability conditions, which belong to the term of Sum-Of-Squares (SOS) form. Based on the Lyapunov function, two stability criteria are proposed to design the corresponding PDPF controller, such that the NTV system is robustly stable and passive. Finally, two examples are applied to demonstrate the effectiveness of the proposed stability criterion. Full article
(This article belongs to the Special Issue Advanced Methods in Fuzzy Control and Their Applications)
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