Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.0
Journals
Axioms
Special Issues
Type-2 Fuzzy Logic: Theory, Algorithms and Applications
share announcement

Type-2 Fuzzy Logic: Theory, Algorithms and Applications

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: closed (20 December 2019) | Viewed by 21497

Special Issue Editor

Prof. Dr. Oscar Castillo

E-Mail Website
Guest Editor
Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, Mexico
Interests: type-2 fuzzy logic; fuzzy control; neuro-fuzzy; genetic-fuzzy hybrid approaches
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 1965, Prof. L. Zadeh introduced the concept of fuzzy sets (FSs) to represent uncertain system parameters. However, in many real-world systems, uncertainty appears for multiple reasons. In such a scenario, uncertainty modelling capabilities of type 1 (T1) or traditional FSs are quite limited, so Zadeh himself came up with the concept of type-2 FSs in 1975. However, for more than a decade, these types of FSs got very little attention from the scientific community. Interestingly, from 1990, researchers started investigating the T2 FSs, or more specifically the interval type-2 (IT2) FSs, and successfully applied the same concept for realistic uncertainty modelling in a number of applications.

Very recently, a new research trend has been noticed, in which researchers have shifted their focus from the IT2 FSs to the general type 2 (GT2) FSs and explored better results in many applications. This has further been motivated by some of Prof. J. M. Mendel´s recent works, in which he has nicely shown that if proper care is taken during the designing phase, an IT2 fuzzy logic system (FLS) shall always produce better (or at least equal) performance than a T1 FLS. Similarly, a GT2 FLS has the capability to give better than (or at least equal performance to) a IT2 FLS. Nevertheless, the growth of research carried out on the T2 FSs and T2 FLSs is far less than the volume of research conducted on T1 FSs. Therefore, this Special Issue aims to introduce cutting-edge research concepts on T2 FSs and systems and their applications in a number of emerging systems including (but not limited to) the following:

  • T2 FS-based uncertainty modelling in Cyber-physical systems
  • Social network analysis under T2 fuzzy uncertainty
  • T2 FLSs in cyber security
  • T2 FS-based uncertainty modelling in big data analytics
  • Multi-media applications with fuzzy uncertainty
  • T2 FSs for image processing
  • T2 FSs in evolutionary optimization
  • T2 FSs and T2 FLSs in machine learning
  • T2 FSs and T2 FLSs deep learning
  • T2 FLSs for power systems
  • T2 FSs for energy optimization
  • T2 FSs and T2 FLSs green computing
  • T2 FS-based uncertainty modelling vehicle routing problem
  • And other application areas with T2 FS-based uncertainty modelling

Prof. Dr. Oscar Castillo
Guest Editor

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. Axioms is an international peer-reviewed open access monthly 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 2400 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

  • type-2 fuzzy logic
  • type-2 fuzzy control
  • type-2 fuzzy pattern recognition
  • type-2 fuzzy neural networks
  • type-2 fuzzy in metaheuristics

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 1011 KiB  
Article
Optimization of the Time-Dependent Traveling Salesman Problem Using Interval-Valued Intuitionistic Fuzzy Sets
by Ruba Almahasneh, Boldizsár Tüű-Szabó, László T. Kóczy and Péter Földesi
Axioms 2020, 9(2), 53; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms9020053 - 13 May 2020
Cited by 13 | Viewed by 2700
Abstract
This study proposes a new model and approach for solving a realistic extension of the Time-Dependent Traveling Salesman Problem, by using the concept of distance between interval-valued intuitionistic fuzzy sets. For this purpose, we developed an interval-valued fuzzy degree repository based on the [...] Read more.
This study proposes a new model and approach for solving a realistic extension of the Time-Dependent Traveling Salesman Problem, by using the concept of distance between interval-valued intuitionistic fuzzy sets. For this purpose, we developed an interval-valued fuzzy degree repository based on the relations between rush hour periods and traffic regions in the “city center areas”, and then we utilized the interval-valued intuitionistic fuzzy weighted arithmetic average to aggregate fuzzy information to be able to quantify the delay in any given trip between two nodes (cities). The proposed method is illustrated by a simple numerical example. Full article
(This article belongs to the Special Issue Type-2 Fuzzy Logic: Theory, Algorithms and Applications)
►▼ Show Figures

Figure 1

16 pages, 7224 KiB  
Article
Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control
by Mahmut Dirik, Oscar Castillo and Adnan Fatih Kocamaz
Axioms 2019, 8(2), 58; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms8020058 - 10 May 2019
Cited by 28 | Viewed by 4924
Abstract
Mobile robot motion planning in an unstructured, static, and dynamic environment is faced with a large amount of uncertainties. In an uncertain working area, a method should be selected to address the existing uncertainties in order to plan a collision-free path between the [...] Read more.
Mobile robot motion planning in an unstructured, static, and dynamic environment is faced with a large amount of uncertainties. In an uncertain working area, a method should be selected to address the existing uncertainties in order to plan a collision-free path between the desired two points. In this paper, we propose a mobile robot path planning method in the visualize plane using an overhead camera based on interval type-2 fuzzy logic (IT2FIS). We deal with a visual-servoing based technique for obstacle-free path planning. It is necessary to determine the location of a mobile robot in an environment surrounding the robot. To reach the target and for avoiding obstacles efficiently under different shapes of obstacle in an environment, an IT2FIS is designed to generate a path. A simulation of the path planning technique compared with other methods is performed. We tested the algorithm within various scenarios. Experiment results showed the efficiency of the generated path using an overhead camera for a mobile robot. Full article
(This article belongs to the Special Issue Type-2 Fuzzy Logic: Theory, Algorithms and Applications)
►▼ Show Figures

Figure 1

21 pages, 7922 KiB  
Article
Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment
by Emer Bernal, Oscar Castillo, José Soria and Fevrier Valdez
Axioms 2019, 8(1), 26; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms8010026 - 25 Feb 2019
Cited by 31 | Viewed by 3596
Abstract
Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation [...] Read more.
Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation cycles. In this paper we present a modification to galactic swarm optimization using type-1 (T1) and interval type-2 (IT2) fuzzy systems for the dynamic adjustment of the c3 and c4 parameters in the algorithm. In addition, the modification is used for the optimization of the fuzzy controller of an autonomous mobile robot. First, the galactic swarm optimization is tested for fuzzy controller optimization. Second, the GSO algorithm with the dynamic adjustment of parameters using T1 fuzzy systems is used for the optimization of the fuzzy controller of an autonomous mobile robot. Finally, the GSO algorithm with the dynamic adjustment of parameters using the IT2 fuzzy systems is applied to the optimization of the fuzzy controller. In the proposed approaches, perturbation (noise) was added to the plant in order to find out if our approach behaves well under perturbation to the autonomous mobile robot plant; additionally, we consider our ability to compare the results obtained with the approaches when no perturbation is considered. Full article
(This article belongs to the Special Issue Type-2 Fuzzy Logic: Theory, Algorithms and Applications)
►▼ Show Figures

Figure 1

21 pages, 4553 KiB  
Article
PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights
by Fernando Gaxiola, Patricia Melin, Fevrier Valdez, Juan R. Castro and Alain Manzo-Martínez
Axioms 2019, 8(1), 14; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms8010014 - 24 Jan 2019
Cited by 22 | Viewed by 4349
Abstract
A dynamic adjustment of parameters for the particle swarm optimization (PSO) utilizing an interval type-2 fuzzy inference system is proposed in this work. A fuzzy neural network with interval type-2 fuzzy number weights using S-norm and T-norm is optimized with the proposed method. [...] Read more.
A dynamic adjustment of parameters for the particle swarm optimization (PSO) utilizing an interval type-2 fuzzy inference system is proposed in this work. A fuzzy neural network with interval type-2 fuzzy number weights using S-norm and T-norm is optimized with the proposed method. A dynamic adjustment of the PSO allows the algorithm to behave better in the search for optimal results because the dynamic adjustment provides good synchrony between the exploration and exploitation of the algorithm. Results of experiments and a comparison between traditional neural networks and the fuzzy neural networks with interval type-2 fuzzy numbers weights using T-norms and S-norms are given to prove the performance of the proposed approach. For testing the performance of the proposed approach, some cases of time series prediction are applied, including the stock exchanges of Germany, Mexican, Dow-Jones, London, Nasdaq, Shanghai, and Taiwan. Full article
(This article belongs to the Special Issue Type-2 Fuzzy Logic: Theory, Algorithms and Applications)
►▼ Show Figures

Figure 1

35 pages, 9964 KiB  
Article
Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification
by Juan Carlos Guzmán, Ivette Miramontes, Patricia Melin and German Prado-Arechiga
Axioms 2019, 8(1), 8; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms8010008 - 15 Jan 2019
Cited by 42 | Viewed by 5038
Abstract
The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have [...] Read more.
The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work. Full article
(This article belongs to the Special Issue Type-2 Fuzzy Logic: Theory, Algorithms and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop