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Mathematics
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Advanced Research in Fuzzy Systems and Artificial Intelligence
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Advanced Research in Fuzzy Systems and Artificial Intelligence

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: 30 June 2024 | Viewed by 1890

Special Issue Editors

Dr. Alexey Filippov

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Guest Editor
Department of Information Systems, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia
Interests: knowledge engineering; data mining; fuzzy systems
Prof. Dr. Vilém Novák

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Guest Editor
Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, 30. dubna 22, 702 00 Ostrava, Czech Republic
Interests: mathematical fuzzy logic and applications
Special Issues, Collections and Topics in MDPI journals
Dr. Anton Romanov

E-Mail Website
Guest Editor
Department of Information Systems, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia
Interests: time series data mining; fuzzy control; fuzzy systems

Special Issue Information

Dear Colleagues,

Zadeh introduced fuzzy set theory in 1965, and many researchers have developed improved fuzzy methods to use in various fields of artificial intelligence, such as fuzzy control and multi-criteria decision making.

The goal of this Special Issue is to attract the latest results from researchers working on theoretical advances in fuzzy sets, fuzzy systems, and decision making and the corresponding industry applications. These results can be obtained from the research of new theoretical knowledge on the discipline itself, or via the construction of fuzzy-/hybrid-based solutions to solve various interdisciplinary tasks.

In this Special Issue, we encourage submissions providing new results in the setting of fuzzy systems and their applications in the artificial intelligence field. Potential topics include, but are not limited to, the next keywords (see below).

Dr. Alexey Filippov
Prof. Dr. Vilém Novák
Dr. Anton Romanov
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

  • mathematics of fuzzy sets
  • type-1 fuzzy logic/models/applications
  • theoretical analysis and hybridizations in fuzzy logic
  • fuzzy control
  • fuzzy neural systems
  • genetic fuzzy systems
  • fuzzy modeling/intelligent data analysis
  • fuzzy database mining and forecasting
  • FL-based decision modeling
  • decision making under uncertainty
  • consensus modeling
  • aggregation operators of uncertain information
  • adaptive fuzzy systems/uncertainty management
  • computational intelligence/learning
  • pattern recognition/image processing
  • soft computing in database management and information retrieval

Published Papers (2 papers)

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14 pages, 927 KiB  
Article
A Multi-Objective Optimization-Algorithm-Based ANFIS Approach for Modeling Dynamic Customer Preferences with Explicit Nonlinearity
by Huimin Jiang and Farzad Sabetzadeh
Mathematics 2023, 11(21), 4559; https://doi.org/10.3390/math11214559 - 06 Nov 2023
Viewed by 700
Abstract
In previous studies, customer preferences were assumed to be static when modeling their preferences based on online reviews. However, in fact, customer preferences for products are dynamic and changing over time. Few research has been conducted to model dynamic customer preferences as the [...] Read more.
In previous studies, customer preferences were assumed to be static when modeling their preferences based on online reviews. However, in fact, customer preferences for products are dynamic and changing over time. Few research has been conducted to model dynamic customer preferences as the time series data of customer preference are difficult to be obtained. Based on online reviews, an adaptive neuro fuzzy inference system (ANFIS) was introduced to model customer preferences, which can take into account the fuzzy nature of customers’ emotions and the nonlinearity of the model. However, ANFIS is plagued with black box problems, and the nonlinearity of the model cannot be directly demonstrated. To address the above research issues, a multi-objective chaos optimization algorithm (MOCOA)-based ANFIS approach is proposed to generate customer preferences models by using online reviews, which has explicit nonlinear inputs. Firstly, a sentiment analysis approach is used to derive information from online reviews by periods, which is used as the time series data sets of the proposed model. A MOCOA is combined into ANFIS to identify the nonlinear inputs, which include single items, interactive items, and terms of second order and/or higher-order terms. Consequently, the fuzzy rules in ANFIS are expressed in polynomial form, which allows for the explicit representation of the nonlinearity between customer preferences and product attributes. A case study of sweeping robots is used to compare the validation results of the proposed approach with those of ANFIS, subtractive cluster-based ANFIS, fuzzy c-means-based ANFIS, and K-means-based ANFIS. Moreover, the proposed approach provides better performance than the other four approaches in terms of mean relative error and variance of error. Full article
(This article belongs to the Special Issue Advanced Research in Fuzzy Systems and Artificial Intelligence)
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13 pages, 377 KiB  
Article
SVD-Based Identification of Parameters of the Discrete-Time Stochastic Systems Models with Multiplicative and Additive Noises Using Metaheuristic Optimization
by Andrey Tsyganov and Yulia Tsyganova
Mathematics 2023, 11(20), 4292; https://doi.org/10.3390/math11204292 - 15 Oct 2023
Cited by 2 | Viewed by 730
Abstract
The paper addresses a parameter identification problem for discrete-time stochastic systems models with multiplicative and additive noises. Stochastic systems with additive and multiplicative noises are considered when solving many practical problems related to the processing of measurements information. The purpose of this work [...] Read more.
The paper addresses a parameter identification problem for discrete-time stochastic systems models with multiplicative and additive noises. Stochastic systems with additive and multiplicative noises are considered when solving many practical problems related to the processing of measurements information. The purpose of this work is to develop a numerically stable gradient-free instrumental method for solving the parameter identification problems for a class of mathematical models described by discrete-time linear stochastic systems with multiplicative and additive noises on the basis of metaheuristic optimization and singular value decomposition. We construct an identification criterion in the form of the negative log-likelihood function based on the values calculated by the newly proposed SVD-based Kalman-type filtering algorithm, taking into account the multiplicative noises in the equations of the state and measurements. Metaheuristic optimization algorithms such as the GA (genetic algorithm) and SA (simulated annealing) are used to minimize the identification criterion. Numerical experiments confirm the validity of the proposed method and its numerical stability compared with the usage of the conventional Kalman-type filtering algorithm. Full article
(This article belongs to the Special Issue Advanced Research in Fuzzy Systems and Artificial Intelligence)
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