Special Issue "Type-2 Fuzzy Logic: Theory, Algorithms and Applications"

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: 20 December 2019.

Special Issue Editor

Prof. Dr. Oscar Castillo
E-Mail Website
Guest Editor
Division of Graduate Studies and Research, Tijuana Institute of Technology, 22414 Tijuana, Mexico
Interests: fuzzy logic; type-2 fuzzy logic; fuzzy control; hybrid intelligent systems
Special Issues and Collections 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

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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 (4 papers)

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Research

Open AccessArticle
Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control
Axioms 2019, 8(2), 58; https://doi.org/10.3390/axioms8020058 - 10 May 2019
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)
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Open AccessArticle
Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment
Axioms 2019, 8(1), 26; https://doi.org/10.3390/axioms8010026 - 25 Feb 2019
Cited by 5
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)
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Open AccessArticle
PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights
Axioms 2019, 8(1), 14; https://doi.org/10.3390/axioms8010014 - 24 Jan 2019
Cited by 4
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)
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Open AccessArticle
Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification
Axioms 2019, 8(1), 8; https://doi.org/10.3390/axioms8010008 - 15 Jan 2019
Cited by 4
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)
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