IEEE Transactions on Fuzzy Systems

The papers in this special section focus on recent developments and emerging topics in the area of deep fuzzy models that address some of the problems and limitations above. These models have been known under different names, such as hierarchical fuzzy ...

Although data preprocessing is a universal technique that can be widely used in neural networks (NNs), most research in this area is focused on designing new NN architectures. This paper, we propose a preprocessing technique that enriches the original ...

An echo state network (ESN) is a recurrent neural network with low computational complexity. However, a single ESN cannot extract effective features from complex inputs, especially for dealing with low-cost condition signals in machinery fault diagnosis. ...

This article investigates the issues of passivity analysis and feedback passification for a class of switched Takagi–Sugeno (T–S) fuzzy systems with the sampled-data-dependent switching strategy and controllers. Different from the previous ...

In this article, the fuzzy property is described by means of the Z-number as the coefficients and variables of the fuzzy equations. This alteration for the fuzzy equation is appropriate for system modeling with Z-number parameters. In this article, the ...

Research has shown that the human lip and its movements are a rich source of information related to speech content and speaker's identity. Lip image segmentation, as a fundamental step in many lip-reading and visual speaker authentication systems, ...

A fuzzy deep neural network with sparse autoencoder (FDNNSA) is proposed for intention understanding based on human emotions and identification information (i.e., age, gender, and region), in which the fuzzy C-means (FCM) is used to cluster the input data,...

A fuzzy opinion is a Gaussian fuzzy set with the center representing the opinion and the standard deviation representing the uncertainty about the opinion, and a fuzzy opinion network is a connection of a number of fuzzy opinions in a structured way. In ...

As an alternative to existing construction methods of Takagi&#x2013;Sugeno&#x2013;Kang (TSK) fuzzy classifiers, this paper presents a novel design methodology formulated by a new concept called <italic>fuzzy-knowledge-out</italic> and its induced wide ...

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous ...

A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multilayer connection of many low-dimensional fuzzy systems, where the input variables to the low-dimensional fuzzy systems are selected through a moving window across the ...

Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This article proposes a novel self-organizing deep FNN, namely deep evolving fuzzy neural ...

Software vulnerability has long been an important but critical research issue in cybersecurity. Recently, the machine learning (ML)-based approach has attracted increasing interest in the research of software vulnerability detection. However, the ...

We establish a fuzzy deep model called the fuzzy deep belief net (FDBN) based on fuzzy restricted Boltzmann machines (FRBMs) due to their excellent generative and discriminative properties. The learning procedure of an FDBN is divided into a pretraining ...

Unsupervised person reidentification has received more attention due to its wide real-world applications. In this paper, we propose a novel method named fuzzy multilayer clustering (FMC) for unsupervised person reidentification. The proposed FMC learns a ...

Different from our previous stacked-structure-based deep fuzzy classifier, in this paper, we explore the distinctive role of adversarial outputs of training samples in enhancing the classification performance of a stacked-structure-based deep fuzzy ...

This paper presents a&#x00A0;time-series classification method based on fuzzy cognitive maps. We advocate that fuzzy cognitive maps provide a&#x00A0;sound representation of time series, and we can construct a&#x00A0;classification mechanism based on them. ...

Deep learning models often use a flat softmax layer to classify samples after feature extraction in visual classification tasks. However, it is hard to make a single decision of finding the true label from massive classes. In this scenario, hierarchical ...

While deep learning has proven to be a powerful new tool for modeling and predicting a wide variety of complex phenomena, those models remain incomprehensible black boxes. This is a critical impediment to the widespread deployment of deep learning ...

Fuzzy clustering is a classical approach to provide the soft partition of data. Although its enhancements have been intensively explored, fuzzy clustering still suffers from the difficulties in handling real high-dimensional data with complex latent ...

This paper is concerned with the problem of interval type-2 (IT2) fuzzy sampled-data <inline-formula><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> control for nonlinear networked control systems with parameter uncertainties, data ...

This paper develops a new method for group decision making with hesitant fuzzy preference relations (HFPRs) considering the multiplicative consistency and consensus simultaneously. A consistency index of HFPR is introduced and the acceptable consistent ...

A class of generalized nonlinear dynamic systems is first approximated by <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> fuzzy-based linear subsystems using the identification of input&#x2013;output data or the linearizing ...

Multicriteria decision making (MCDM) has become one of the most frequently applied decision making methodologies in various fields. However, uncertainty is inevitably involved in the process of MCDM due to the subjectivity of humans. To address this issue,...

This article presents an online learning method for improved control of nonlinear systems by combining deep learning and fuzzy logic. Given the ability of deep learning to generalize knowledge from training samples, the proposed method requires minimum ...

A robust fuzzy predictive control (RFPC) based on Takagi&#x2013;Sugeno (T-S) fuzzy model is proposed for systems with uncertainties, time-varying delays, unknown disturbances, as well as strong nonlinearity. First, the T-S fuzzy model is built by a number ...