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IEEE Transactions on Fuzzy Systems
Year: 2020 | Volume: 28 | Issue: 7
Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function
Publication Year: 2020,Page(s):1195 - 1204
Cited by: Papers (30)
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 image data using local intensity information; this technique is motivated by human perception. To encode this information into an image, we introduc...Show More
Deep Fuzzy Echo State Networks for Machinery Fault Diagnosis
Shaohui Zhang;Zhenzhong Sun;Man Wang;Jianyu Long;Yun Bai;Chuan Li
Publication Year: 2020,Page(s):1205 - 1218
Cited by: Papers (61)
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. A novel deep learning model, referred to as the deep fuzzy ESN (DFESN), was proposed to improve the feature extraction capability with less compu...Show More
Deep Fuzzy Echo State Networks for Machinery Fault Diagnosis
Shaohui Zhang;Zhenzhong Sun;Man Wang;Jianyu Long;Yun Bai;Chuan Li
Year: 2020 | Volume: 28 | Issue: 7
Passivity and Passification for Switched T–S Fuzzy Systems With Sampled-Data Implementation
Publication Year: 2020,Page(s):1219 - 1229
Cited by: Papers (20)
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 switching laws, the proposed switching law only requires the values of system state at the discrete sampling instants. More incisively, a dwell time constraint ...Show More
Neural Network Approach to Solving Fuzzy Nonlinear Equations Using Z-Numbers
Publication Year: 2020,Page(s):1230 - 1241
Cited by: Papers (23)
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 fuzzy equation with Z-number coefficients and variables is tended to be used as the models for the uncertain systems. The modeling issue related to...Show More
Lip Image Segmentation Based on a Fuzzy Convolutional Neural Network
Publication Year: 2020,Page(s):1242 - 1251
Cited by: Papers (31)
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, is of vital importance. Because of variations in lip color, lighting conditions and especially the complex appearance of an open mouth, accurate lip reg...Show More
Lip Image Segmentation Based on a Fuzzy Convolutional Neural Network
Year: 2020 | Volume: 28 | Issue: 7
A Fuzzy Deep Neural Network With Sparse Autoencoder for Emotional Intention Understanding in Human–Robot Interaction
Publication Year: 2020,Page(s):1252 - 1264
Cited by: Papers (37)
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, and deep neural network with sparse autoencoder (DNNSA) is designed for emotional intention understanding in human-robot interaction. It aims t...Show More
Hierarchical Fuzzy Opinion Networks: Top–Down for Social Organizations and Bottom–Up for Election
Publication Year: 2020,Page(s):1265 - 1275
Cited by: Papers (23)
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 this article, we propose a top-down hierarchical fuzzy opinion network to model how the opinion of a top leader is penetrated into the members in ...Show More
Biologically Plausible Fuzzy-Knowledge-Out and Its Induced Wide Learning of Interpretable TSK Fuzzy Classifiers
Publication Year: 2020,Page(s):1276 - 1290
Cited by: Papers (23)
As an alternative to existing construction methods of Takagi-Sugeno-Kang (TSK) fuzzy classifiers, this paper presents a novel design methodology formulated by a new concept called fuzzy-knowledge-out and its induced wide learning way. Analogous to the “dropout” concept in deep learning, the concept of fuzzy-knowledge-out in TSK fuzzy classifiers is motivated by the firing pattern of knowledge in b...Show More
Enabling Explainable Fusion in Deep Learning With Fuzzy Integral Neural Networks
Muhammad Aminul Islam;Derek T. Anderson;Anthony J. Pinar;Timothy C. Havens;Grant Scott;James M. Keller
Publication Year: 2020,Page(s):1291 - 1300
Cited by: Papers (51)
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 decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the...Show More
Fast Training Algorithms for Deep Convolutional Fuzzy Systems With Application to Stock Index Prediction
Publication Year: 2020,Page(s):1301 - 1314
Cited by: Papers (31)
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 input spaces of the layers. To design the DCFS based on an input–output data pairs, we propose a bottom–up layer-by-layer scheme. Specifically, by v...Show More
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Nonstationary Data Streams
Publication Year: 2020,Page(s):1315 - 1328
Cited by: Papers (23)
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 network (DEVFNN). Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The struc...Show More
DeepBalance: Deep-Learning and Fuzzy Oversampling for Vulnerability Detection
Shigang Liu;Guanjun Lin;Qing-Long Han;Sheng Wen;Jun Zhang;Yang Xiang
Publication Year: 2020,Page(s):1329 - 1343
Cited by: Papers (51)
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 detection performance of existing ML-based methods require further improvement. There are two challenges: one is code representation for ML and the other...Show More
A Fuzzy Deep Model Based on Fuzzy Restricted Boltzmann Machines for High-Dimensional Data Classification
Publication Year: 2020,Page(s):1344 - 1355
Cited by: Papers (37)
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 phase and a subsequent fine-tuning phase. In the pretraining phase, a group of FRBMs is trained in a greedy layerwise way: the first FRBM is train...Show More
Fuzzy Multilayer Clustering and Fuzzy Label Regularization for Unsupervised Person Reidentification
Zhong Zhang;Meiyan Huang;Shuang Liu;Baihua Xiao;Tariq S. Durrani
Publication Year: 2020,Page(s):1356 - 1368
Cited by: Papers (22)
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 new feature space using a multilayer perceptron for clustering in order to overcome the influence of complex pedestrian images. Meanwhile, the pr...Show More
A Novel Deep Fuzzy Classifier by Stacking Adversarial Interpretable TSK Fuzzy Sub-Classifiers With Smooth Gradient Information
Publication Year: 2020,Page(s):1369 - 1382
Cited by: Papers (26)
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 classifier. In order to achieve such goals, an adversarial Takagi-Sugeno-Kang (TSK) fuzzy classifier, which is denoted as TSKa, is proposed. With the T...Show More
Time-Series Classification Using Fuzzy Cognitive Maps
Publication Year: 2020,Page(s):1383 - 1394
Cited by: Papers (13)
This paper presents a time-series classification method based on fuzzy cognitive maps. We advocate that fuzzy cognitive maps provide a sound representation of time series, and we can construct a classification mechanism based on them. The classifier has to distinguish maps constructed for time series belonging to different classes. The proposed classification procedure evaluates similarity of fuzz...Show More
Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification
Yu Wang;Qinghua Hu;Pengfei Zhu;Linhao Li;Bingxu Lu;Jonathan M. Garibaldi;Xianling Li
Publication Year: 2020,Page(s):1395 - 1406
Cited by: Papers (12)
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 classification is proved to be an effective solution and can be utilized to replace the softmax layer. A key issue of hierarchical classification ...Show More
Interpretable Deep Convolutional Fuzzy Classifier
Publication Year: 2020,Page(s):1407 - 1419
Cited by: Papers (36)
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 technology, as decades of research have found that users simply will not trust (i.e., make decisions based on) a model whose solutions cannot be explain...Show More
Deep Fuzzy Clustering—A Representation Learning Approach
Publication Year: 2020,Page(s):1420 - 1433
Cited by: Papers (47)
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 distribution. To solve the problem, this article proposes a deep fuzzy clustering method by representing the data in a feature space produced by the d...Show More
Interval Type-2 Fuzzy Sampled-Data $H_{\infty }$ Control for Nonlinear Unreliable Networked Control Systems
Publication Year: 2020,Page(s):1434 - 1448
Cited by: Papers (88)
This paper is concerned with the problem of interval type-2 (IT2) fuzzy sampled-data H∞ control for nonlinear networked control systems with parameter uncertainties, data dropout, and transmission delay. The IT2 fuzzy system used to describe the networked control systems and the IT2 networked sampled-data controller implemented by a zero-order holder is connected in the closed-loop system. By mean...Show More
A New Method for Group Decision Making With Hesitant Fuzzy Preference Relations Based on Multiplicative Consistency
Publication Year: 2020,Page(s):1449 - 1463
Cited by: Papers (20)
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 HFPR is defined. For improving the unacceptable consistent HFPR, a mathematical program is constructed to derive an acceptable consistent HFPR. Thu...Show More
Fuzzy Fixed-Time Learning Control With Saturated Input, Nonlinear Switching Surface, and Switching Gain to Achieve Null Tracking Error
Publication Year: 2020,Page(s):1464 - 1476
Cited by: Papers (21)
A class of generalized nonlinear dynamic systems is first approximated by N fuzzy-based linear subsystems using the identification of input-output data or the linearizing system with respect to suitable operating points. To obtain null trajectory tracking error in fixed time, a fuzzy fixed-time control (FFTC) with nonlinear switching surface and switching gain is first designed. It can be said tha...Show More
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