Presents the table of contents for this issue of this publication.

Provides a listing of current staff, committee members and society officers.

Advertisement.

Provides a listing of current committee members and society officers.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Advertisement.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of this publication.

Provides a listing of current committee members and society officers.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The purpose of this article is twofold. First, and foremost, it fixes an error that somehow made it through all the reviewing, both by the authors and the referees. Second, it provides insights into the meaning and variation of the main PCM parameters in this approach to transfer clustering.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The articles in this special section are dedicated to the memory of Prof. Robert John, one of the pioneers of type-2 fuzzy sets and systems, who passed away during the preparation of this issue.

Advertisement.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

This paper addresses the problem of continuoustime dynamic sliding mode control for Takagi'Sugeno fuzzy nonlinear systems based on an event-triggered mechanism. First, a novel sliding mode dynamic system is established by introducing a corresponding sliding mode function and an efficient eventtriggered mechanism. Then, based on a new Lyapunov function and the reciprocally convex method, sufficient

This paper proposes a high order disturbance observer (HODO) and dynamic surface control (DSC) technique based adaptive fuzzy control scheme for nonlinear systems subjected to input saturation and external time-varying disturbances. Firstly, based on a Sigmoid function, the saturation input is tackled by utilizing a well-defined nonlinear smooth function. Furthermore, HODO and fuzzy logic systems (FLSs)

Nowadays, large-scale group decision making is usually handled based on clustering analysis process (CAP) and consensus reaching process (CRP). However, CAP and CRP can be contradictory since CAP is performed based on differences between potentially small groups and CRP is conducted to improve the overall similarity of a large group. To balance CAP and CRP, we propose a dynamic clustering analysis

This paper studies the bipartite containment control problem for nonlinear multi-agent systems (MASs) with input quantization over a signed digraph. The design objective is to provide an appropriate distributed protocol, such that the followers converge to a convex hull containing each leader trajectory as well as its opposite trajectory different in sign. Based on a nonlinear decomposition of input

Fuzzy sets were initially proposed to address ambiguities and uncertainties. However, in certain cases, the fuzzy sets show some degree of uncertainty and risk, when the available data are either obtained from unreliable sources or related to future events. To solve this problem, the R-numbers methodology has been recently developed as a powerful approach to model the risk of fuzzy sets and numbers

This paper investigates the problem of multistability of almost-periodic solutions of Takagi-Sugeno fuzzy neural networks with nonmonotonic discontinuous activation functions and time-varying delays. Based on the geometrical properties of the nonmonotonic activation functions, by using Ascoli-Arzela theorem and the inequality techniques, it is demonstrated that under some reasonable conditions, the

Type-2 fuzzy sets (T2FSs) were introduced by Zadeh in 1975 as an extension of type-1 fuzzy sets. The membership grades of type-2 fuzzy sets are type-1 fuzzy sets in interval [0; 1], which are referred to as fuzzy truth values (that is, functions from [0; 1] to [0; 1]). Recently, (convolution) operations on type-2 fuzzy sets become a hot research topic, such as type-2 t-norms, type-2 aggregation operations

Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction. This paper proposes a method to construct classifiers from multi-resolution hierarchical granular representations (MRHGRC) using

In the present study, a new iterative numerical method for solving bivariate nonlinear fuzzy Fredholm integral equations is proposed. The method combines two well proven approaches: successive approximations and mixed trapezoidal and midpoint rules for the numerical integration. Both approaches are elaborated for fuzzy-valued functions. We give conditions for the existence and uniqueness of a solution

Presents the table of contents for this issue of this publication.

Provides a listing of current staff, committee members and society officers.

The papers in this special section focus on applications of fuzzy systems in data science and Big Data. In the era of Big Data, intelligent as well as fuzzy tools have become very important to understanding our changing Internet-of-Things-driven data-centric world. Extracting “intelligence” from massive amounts of data has allowed us to support decision making processes in many fields, ranging from

Provides instructions and guidelines to prospective authors who wish to submit manuscripts.

Provides a listing of current committee members and society officers.

For optimal utilization of power generation resources, load forecasting plays a vital role in balancing the load flow in a power distribution network. There are several drawbacks associated with existing forecasting techniques for load flow balancing. Neural network (NN) based forecasting techniques are unable to consider the actual states of a power system, while weighted least squares state estimation

The continuous development of industry big data technology requires better computing methods to discover the data value. Information forecast, as an important part of data mining technology, has achieved excellent applications in some industries. However, the existing deviation and redundancy in the data collected by the sensors make it difficult for some methods to accurately predict future information

For heterogeneous data sets containing numerical and symbolic feature values, feature selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very significant step to preprocess data and improve its classification performance. This article presents an FNMRS-based feature selection approach in neighborhood decision systems. First, some concepts of fuzzy neighborhood rough sets

Traditional deep learning methods are suboptimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always

Heterogeneous big data poses many challenges in machine learning. Its enormous scale, high dimensionality, and inherent uncertainty make almost every aspect of machine learning difficult, from providing enough processing power to maintaining model accuracy to protecting privacy. However, perhaps the most imposing problem is that big data is often interspersed with sensitive personal data. Hence, we

It is agreed that portfolio selection models are of great importance for the financial market. In this article, a constrained multiperiod multiobjective portfolio model is established. This model introduces several constraints to reflect the trading restrictions and quantifies future security returns by fuzzy random variables to capture fuzzy and random uncertainties in the financial market. Meanwhile

Topological data analysis is a new theoretical trend using topological techniques to mine data. This approach helps determine topological data structures. It focuses on investigating the global shape of data rather than on local information of high-dimensional data. The Mapper algorithm is considered as a sound representative approach in this area. It is used to cluster and identify concise and meaningful

Machine learning (ML) has emerged as a powerful tool for pattern recognition. Traditional ML algorithms have limited ability to reveal the most sophisticated features of cancer histopathological images, but their robustness and fault tolerance can be enhanced by using fuzzy modeling to capture the uncertainty in image data. Therefore, this article proposes a novel CoMHisP framework based on a fuzzy

In this article, a novel fuzzy-based dynamic multiobjective evolutionary algorithm is presented. In this article, giving a valid and true response to the change is an essential task to improve the diversity of solutions when an environmental change occurs. The basic idea is to randomly remove some solutions and replace by newly created solutions. However, the random selection detours the algorithm's

In this article, analogies and differences between a type of fuzzy transform and a type of autoencoder, both based on a least-squares optimization, will be discussed. Such schemes have been recently introduced in the literature in different contexts. In particular, in this article, the data compression application will be considered. As it will be shown, the least-squares fuzzy transform can be regarded

The vehicular ad hoc network (VANET) is a type of mobile wireless networks, where vehicles are allowed to broadcast a message to its neighbors and access data from other participants. However, how to guarantee the reliability of these broadcast messages and prevent malicious vehicles from accessing the private data of the VANETs is still an open problem to be solved. As a countermeasure, a fuzzy-based

Medical Internet of Things are generating a big volume of data to enable smart medicine that tries to offer computer-aided medical and healthcare services with artificial intelligence techniques like deep learning and clustering. However, it is a challenging issue for deep learning, and clustering algorithms to analyze large medical data because of their high computational complexity, thus hindering

Hashing methods for efficient image retrieval aim at learning hash functions that map similar images to semantically correlated binary codes in the Hamming space with similarity well preserved. The traditional hashing methods usually represent image content by hand-crafted features. Deep hashing methods based on deep neural network (DNN) architectures can generate more effective image features and

Big data analytics and processing require complex architectures and sophisticated techniques for extracting useful information from the accumulated information. Visualizing the extracted data for real-time solutions is demanding in accordance with the semantics and the classification employed by the processing models. This article introduces fuzzy-optimized data management (FDM) technique for classifying

Dempster–Shafer evidence (DSE) theory, which allows combining pieces of evidence from different data sources to derive a degree of belief function that is a type of fuzzy measure, is a general framework for reasoning with uncertainty. In this framework, how to optimally manage the conflicts of multiple pieces of evidence in DSE remains an open issue to support decision making. The existing conflict

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

The eighteen papers in this special section focus on emerging techniques and applications supported by fuzzy logic and artificial intelligence (AI). AI has become the focus of the day and attracted much attention from researchers, industries, and governments. This special issue serves as a forum to bring together all emerging techniques for fuzzy logic and fuzzy set-based AI and foster new advancements

Takagi–Sugeno–Kang (TSK) fuzzy systems are flexible and interpretable machine learning models; however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high. This article proposes a minibatch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy classifiers. It integrates two novel techniques: First, uniform regularization