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Applied Sciences
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Fuzzy Systems and Fuzzy Neural Networks: Theory and Applications
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Fuzzy Systems and Fuzzy Neural Networks: Theory and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 5103

Special Issue Editors

Prof. Dr. Cheng-Jian Lin

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Guest Editor
1. Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
2. College of Intelligence, National Taichung University of Science and Technology, Taichung 40401, Taiwan
Interests: artificial intelligence; smart robots; deep learning; data mining; image processing
Dr. Tsang-Chuan Chang

E-Mail
Guest Editor
College of Intelligence, National Taichung University of Science and Technology, Taichung 40401, Taiwan
Interests: fuzzy statistics; process capability analysis; performance evaluation; quality management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A fuzzy system is any fuzzy logic-based system, which uses fuzzy logic as the basis for knowledge representation, using different forms of knowledge. A fuzzy neural network is a learning machine that finds the parameters of a fuzzy system by exploiting approximation techniques from neural networks. In particular, currently developed theory and applications of fuzzy systems and fuzzy neural networks, and both fuzzy systems and fuzzy neural networks have some things in common. They can be used for solving various types of problems in the areas of economics, business, engineering, management, etc.

The main purpose of this Issue is to publish original research articles covering advances in the modeling of hybrid fuzzy systems, and applications of fuzzy neural networks contributing to engineering,  industries, electronics, and communications.

Potential topics include, but are not limited to, the following:

  • Fuzzy modeling
  • Type-2 Fuzzy system
  • Fuzzy neural networks
  • Hybrid fuzzy model
  • Evolutionary and swarm intelligence methods
  • Fuzzy classification and clustering
  • Fuzzy neural network applications in control and robotics
  • Fuzzy neural network applications in data mining and big data analysis
  • Fuzzy neural network applications in hybrid systems, modeling and simulation
  • Fuzzy neural network applications in Industry 4.0
  • Fuzzy neural network applications in Internet of Things
  • Fuzzy neural network applications in rehabilitation engineering
  • Fuzzy neural network applications in smart car
  • Fuzzy neural network applications in vision and sensors

Prof. Dr. Cheng-Jian Lin
Dr. Tsang-Chuan Chang
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. Applied Sciences 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 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

  • fuzzy modeling
  • type-2 fuzzy system
  • hybrid fuzzy model
  • evolutionary and swarm intelligence methods
  • fuzzy classification and clustering
  • fuzzy neural network applications

Published Papers (3 papers)

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Research

14 pages, 1427 KiB  
Article
Malware Classification Using Convolutional Fuzzy Neural Networks Based on Feature Fusion and the Taguchi Method
by Cheng-Jian Lin, Min-Su Huang and Chin-Ling Lee
Appl. Sci. 2022, 12(24), 12937; https://0-doi-org.brum.beds.ac.uk/10.3390/app122412937 - 16 Dec 2022
Cited by 2 | Viewed by 1216
Abstract
The applications of computer networks are increasingly extensive, and networks can be remotely controlled and monitored. Cyber hackers can exploit vulnerabilities and steal crucial data or conduct remote surveillance through malicious programs. The frequency of malware attacks is increasing, and malicious programs are [...] Read more.
The applications of computer networks are increasingly extensive, and networks can be remotely controlled and monitored. Cyber hackers can exploit vulnerabilities and steal crucial data or conduct remote surveillance through malicious programs. The frequency of malware attacks is increasing, and malicious programs are constantly being updated. Therefore, more effective malware detection techniques are being developed. In this paper, a convolutional fuzzy neural network (CFNN) based on feature fusion and the Taguchi method is proposed for malware image classification; this network is referred to as FT-CFNN. Four fusion methods are proposed for the FT-CFNN, namely global max pooling fusion, global average pooling fusion, channel global max pooling fusion, and channel global average pooling fusion. Data are fed into this network architecture and then passed through two convolutional layers and two max pooling layers. The feature fusion layer is used to reduce the feature size and integrate the network information. Finally, a fuzzy neural network is used for classification. In addition, the Taguchi method is used to determine optimal parameter combinations to improve classification accuracy. This study used the Malimg dataset to evaluate the accuracy of the proposed classification method. The accuracy values exhibited by the proposed FT-CFNN, proposed CFNN, and original LeNet model in malware family classification were 98.61%, 98.13%, and 96.68%, respectively. Full article
(This article belongs to the Special Issue Fuzzy Systems and Fuzzy Neural Networks: Theory and Applications)
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21 pages, 3789 KiB  
Article
A Design and Optimization of a CGK-Based Fuzzy Granular Model Based on the Generation of Rational Information Granules
by Chan-Uk Yeom and Keun-Chang Kwak
Appl. Sci. 2022, 12(14), 7226; https://0-doi-org.brum.beds.ac.uk/10.3390/app12147226 - 18 Jul 2022
Cited by 1 | Viewed by 1194
Abstract
This study proposes an optimized context-based Gustafson Kessel (CGK)-based fuzzy granular model based on the generation of rational information granules and an optimized CGK-based fuzzy granular model with the aggregated structure. The conventional context-based fuzzy-c-means (CFCM) clustering generates clusters considering the input and [...] Read more.
This study proposes an optimized context-based Gustafson Kessel (CGK)-based fuzzy granular model based on the generation of rational information granules and an optimized CGK-based fuzzy granular model with the aggregated structure. The conventional context-based fuzzy-c-means (CFCM) clustering generates clusters considering the input and output spaces. However, the prediction performance decreases when the specific data points with geometric features are used. The CGK clustering solves the above situation by generating valid clusters considering the geometric attributes of data in input and output spaces with the aid of the Mahalanobis distance. However, it is necessary to generate rational information granules (IGs) because there is a significant change in performance according to the context generated in the output space and the shape, size, and several clusters generated in the input space. As a result, the rational IGs are obtained by considering the relationship between the coverage and specificity of IG using the genetic algorithm (GA). Thus, the optimized CGK-based fuzzy granular models with the aggregated structure are designed based on rational IGs. The prediction performance was compared using the two databases to verify the validity of the proposed method. Finally, the experiments revealed that the performance of the proposed method is higher than that of the previous model. Full article
(This article belongs to the Special Issue Fuzzy Systems and Fuzzy Neural Networks: Theory and Applications)
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20 pages, 381 KiB  
Article
Mathematical Models of Diagnostic Information Granules Generated by Scaling Intuitionistic Fuzzy Sets
by Anna Bryniarska
Appl. Sci. 2022, 12(5), 2597; https://0-doi-org.brum.beds.ac.uk/10.3390/app12052597 - 02 Mar 2022
Cited by 3 | Viewed by 1508
Abstract
The paper presents a certain class of the mathematical models of diagnostic information granules describing the fuzzy symptoms-faults relationship. A certain fuzzy diagnostic information retrieval system is described as an application of an expert diagnostic system. Symptoms and faults are fuzzy, and with [...] Read more.
The paper presents a certain class of the mathematical models of diagnostic information granules describing the fuzzy symptoms-faults relationship. A certain fuzzy diagnostic information retrieval system is described as an application of an expert diagnostic system. Symptoms and faults are fuzzy, and with some scaling of the symptom-fault concept pair values. These value pairs can be considered as intuitionistic fuzzy sets for the space of diagnosed objects. In this article, for scaling intuitionistic fuzzy sets (n-ScIFS), the deductive theory is formulated. There the intuitionistic fuzzy sets (IFSs) and the Pythagorean fuzzy sets (PFSs) are generalized to the n-ScIFS objects. The membership and non-membership values, as standard, can be described by the 1:1 scale or the quadratic function scale. However, any power scale n>2 can be used. In this paper, any n-Sc scaling functions retaining the IFSs properties are considered. The n-ScIFS theory introduces a conceptual apparatus analogous to the classical theory of Zadeh fuzzy sets and Yager PFSs, consistently striving, for the first time, to formulate the relational structure of n-ScIFSs as a model of a certain information granule system called here the diagnostic granule system. In addition, power- and linear-repeatable diagnostic granules are defined in the n-ScIFSs structure for serial or parallel diagnosis processes. The information granule base is determined and a diagnostic granule system model produced by this information granule base is shown. Certain algorithms have been given to establish the semantic language of diagnosis describing the system of diagnostic information granules. Full article
(This article belongs to the Special Issue Fuzzy Systems and Fuzzy Neural Networks: Theory and Applications)
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