<![CDATA[ IEEE Transactions on Fuzzy Systems - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 91 2017December 11<![CDATA[Table of Contents]]>256C1C447<![CDATA[IEEE Transactions on Fuzzy Systems]]>256C2C264<![CDATA[Guest Editorial Special Section on Fuzzy Systems in Data Science]]>25613731375204<![CDATA[Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification]]>FSmogfs$^e$+Tun$^e$. In addition, the proposals are able to handle any kind of classification dataset without the necessity of preprocessing. The quality of our approaches is analyzed using statistical analysis and comparing with well-known monotonic classifiers.]]>25613761390799<![CDATA[Probabilistic Fuzzy Classification for Stochastic Data]]>25613911402944<![CDATA[Efficient Multiple Kernel Classification Using Feature and Decision Level Fusion]]>multiple kernel learning (MKL) is a technique used to learn the aggregation of a set of valid kernels into a single (ideally) superior kernel. The aggregation can be done using weighted sums of the precomputed kernels, but determining the summation weights is not a trivial task. Furthermore, MKL does not work well with large datasets because of limited storage space and prediction speed. In this paper, we address all three of these multiple kernel challenges. First, we introduce a new linear feature level fusion technique and learning algorithm, GAMKLp. Second, we put forth three new algorithms, DeFIMKL, DeGAMKL, and DeLSMKL, for nonlinear fusion of kernels at the decision level. To address MKL's storage and speed drawbacks, we apply the Nystrom approximation to the kernel matrices. We compare our methods to a successful and state-of-the-art technique called MKL-group lasso (MKLGL), and experiments on several benchmark datasets show that some of our proposed algorithms outperform MKLGL when applied to support vector machine (SVM)-based classification. However, to no surprise, there does not seem to be a global winner but instead different strategies that a user can employ. Experiments with our kernel approximation method show that we can routinely discard most of the training data and at least double prediction speed without sacrificing classification accuracy. These results suggest that MKL-based classification techniques can be applied to big data efficiently, which is confirmed by an experiment using a large dataset.]]>25614031416997<![CDATA[Takagi–Sugeno Fuzzy Modeling Using Mixed Fuzzy Clustering]]>k-nearest neighbors classifiers in five out of five datasets. Dynamic time warping performs better than the Euclidean distance in one dataset and similarly in the remaining. Given the different nature of time variant and invariant data, the choice of a clustering algorithm that treats data differently should be considered for model construction.]]>25614171429931<![CDATA[Fuzzy Consensus Clustering With Applications on Big Data]]>$c$ -means clustering (piFCM) problem. This helps us to establish an algorithmic framework for FCC with flexible choice of utility functions, and speeds FCC significantly with a FCM-like iterative process of piFCM. To meet the big data challenge, we further parallelize FCC on the Spark platform with both vertical and horizontal segmentation schemes. Extensive experiments on various real-world datasets demonstrate the excellent performance of FCC, even with a majority of poor basic partitions. In particular, our method exhibits interesting potential for big data clustering in two real-life applications concerned with online event detection and overlapping community detection, respectively.]]>25614301445657<![CDATA[Compounding General Purpose Membership Functions for Fuzzy Support Vector Machine Under Noisy Environment]]>$C$-SVM, because the latter gives suboptimal results in the presence of outliers. FSVM's ability to absorb outliers strongly depends on how well the training samples are assigned fuzzy membership values (MVs). Traditionally, the membership functions (MFs) used for the FSVM were custom made for applications, and MFs used for one could, in general, not be used for others. To overcome this, general purpose membership functions (GPMFs) are defined in this paper as those MFs that can universally be used for multiple applications and statistically perform better than $C$-SVM. This paper contributes to the GPMF literature in two stages. This paper first with the help of convex hulls presents limitations that the FSVM faces while treating all samples of a class with a single MF, and recommends differential treatment to data by dividing them into two fuzzy sets: one containing possible nonoutliers and the other containing possible outliers. While possible outliers are modeled with a normal MF, possible nonoutliers are recommended to have a constant MV of “1.” Subsequently, this paper introduces novel GPMFs that use clustering techniques to recognize possible outliers, and use set measures like the Hausdorff distance and pt-set distance for defining new MF heuristics. To establish conclusions, the introduced GPMFs are thoroughly evaluated and statistically compared with earlier GPMFs on 15 real-world benchmark datasets. The results were very encouraging, and showed that the proposed GPMFs not only perform significantly better in treating class noise, but also execute with efficient run time complexity.]]>25614461459497<![CDATA[Incorporating Diversity and Informativeness in Multiple-Instance Active Learning]]> $k$-means clustering algorithm is used to explore the hidden structure of the instances in the feature space of the SVM, and the diversity degree of an unlabeled bag is measured by the number of unique clusters covered by the bag. In the second criterion, the lower approximations in fuzzy rough sets are used to define a new concept named dissimilarity degree, which depicts the uniqueness of an instance so as to measure the diversity degree of a bag. By incorporating the proposed diversity criteria with existing informativeness measurements, new MIAL algorithms are developed, which can select bags with both high informativeness and diversity. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed methods.]]>256146014752599<![CDATA[Fuzzy-Based Information Decomposition for Incomplete and Imbalanced Data Learning]]>25614761490798<![CDATA[Streaming Feature Selection for Multilabel Learning Based on Fuzzy Mutual Information]]>256149115073900<![CDATA[Dynamic Rough-Fuzzy Support Vector Clustering]]>256150815214454<![CDATA[Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)]]>256152215352221<![CDATA[A New Fuzzy Set and Nonkernel SVM Approach for Mislabeled Binary Classification With Applications]]>25615361545207<![CDATA[Learning Large-Scale Fuzzy Cognitive Maps Based on Compressed Sensing and Application in Reconstructing Gene Regulatory Networks]]>25615461560691<![CDATA[A Pythagorean-Type Fuzzy Deep Denoising Autoencoder for Industrial Accident Early Warning]]>25615611575913<![CDATA[Fuzzy Resilient Energy-to-Peak Filtering for Continuous-Time Nonlinear Systems]]>25615761588624<![CDATA[Approximate Reasoning on a Basis of <italic>Z</italic>-Number-Valued If–Then Rules]]>Z-number regarded as an ordered pair Z = (A, B) of fuzzy numbers A and B, where A is a linguistic value of a variable of interest, and B is a linguistic value of probability measure of A, playing a role of its reliability. Unfortunately, up to day, there is no research on approximate reasoning realized on the basis of if–then rules with Z-number-valued antecedents and consequents, briefly, Z- rules. Zadeh addressed this problem as related to an uncharted territory. In this paper, a new approach is developed to study approximate reasoning with Z-rules on a basis of linear interpolation. We provide an application of the approach to job satisfaction evaluation and to students’ educational achievement evaluation problems related to psychological and perceptual issues naturally characterized by imperfect information. The obtained results show applicability and validity of the proposed approach.]]>25615891600838<![CDATA[Takagi–Sugeno Dynamic Neuro-Fuzzy Controller of Uncertain Nonlinear Systems]]>256160116151245<![CDATA[Filtering for Discrete-Time Switched Fuzzy Systems With Quantization]]>$H_infty$ and $l_2$–$l_infty$ filtering design problems for discrete-time nonlinear switched systems with quantized measurements using the Takagi–Sugeno (T–S) fuzzy model. The systems under consideration inherently combine features of the switched hybrid systems and the T–S fuzzy systems. The sector bound approach is employed to deal with quantization effects. Based on the fuzzy-basis-dependent Lyapunov function, sufficient conditions are established such that the filtering error system is stochastically stable and a prescribed noise attenuation level in an $H_infty$ or $l_2$–$l_infty$ sense is achieved. Both numerical and practical examples are provided to show the feasibility and efficiency of the design schemes.]]>25616161628902<![CDATA[Reachable Set Estimation and Decentralized Controller Design for Large-Scale Nonlinear Systems With Time-Varying Delay and Input Constraint]]>25616291643676<![CDATA[Vector t-Norms With Applications]]>f(x, y) = xy (x and y belong to [0, 1]). In addition, we use bilinear frame to define corresponding four types of basic bilinear vector t-norms, which are just counterparts of basic t-norms from many aspects. Mathematically, preference vectors are related to decreasing matrices, and we find the general entry relation for the decreasing matrices, by which we prove that a decreasing matrix is commutative and closed under matrix multiplication. Thus, we finally present the definition of preference multiplication commutative monoid, which is equivalent to product bilinear vector t-norm. We also show illustrative examples and applications of some results.]]>25616441654284<![CDATA[Bayesian Takagi–Sugeno–Kang Fuzzy Classifier]]>256165516712357<![CDATA[Fuzzy-Model-Based Sampled-Data Control of Chaotic Systems: A Fuzzy Time-Dependent Lyapunov–Krasovskii Functional Approach]]>25616721684580<![CDATA[A New Aggregation Method-Based Error Analysis for Decision-Theoretic Rough Sets and Its Application in Hesitant Fuzzy Information Systems]]>25616851697855<![CDATA[Visualizing and Reasoning With Imperfect Time Intervals in 2-D]]>256169817131766<![CDATA[Computing Interval Weights for Incomplete Pairwise-Comparison Matrices of Large Dimension—A Weak-Consistency-Based Approach]]>256171417281178<![CDATA[Regulating Constraint Obedience for Fuzzy Mechanical Systems Based on $beta$-Measure and a General Lyapunov Function]]>$beta$-measure for constraint obedience, which reflects how much this constraint is obeyed. Based on a very general Lyapunov function, a control scheme is proposed to render a twofold performance: guaranteed and optimal. In the guaranteed phase, the $beta$-measure is assured to be uniformly bounded and uniformly ultimately bounded, regardless of the actual value of the uncertainty. In the optimal phase, a fuzzy-theoretic-based performance, by which both the “average” $beta$-measure and control effort are considered, is minimized. As a result, the control serves the practical engineering purposes: The mechanical system is guaranteed to follow the desired task with the minimum cost. This paper is part of a unique endeavor to cast both the descriptions of the uncertainty and desired performance index into a fuzzy framework.]]>256172917401217<![CDATA[Fundamental Properties With Respect to the Completeness of Intuitionistic Fuzzy Partially Ordered Set]]>256174117511402<![CDATA[VD-IT2, Virtual Disk Cloning on Disk Arrays Using a Type-2 Fuzzy Controller]]>256175217642019<![CDATA[Admissibilization of Singular Interval-Valued Fuzzy Systems]]>25617651776678<![CDATA[Mixed Fuzzy Clustering for Misaligned Time Series]]>P time-variant features; and 3) incorporating unsupervised learning of cluster-dependent attribute weights. The algorithm is designed to simultaneously cluster time-variant and time-invariant data. We demonstrate the advantages of the proposed algorithm in four synthetic datasets and in two real-world applications in intensive care units. The first application is the classification of patients who will need the administration of vasopressors, and the second is the classification of patients with a high risk of mortality. Time-variant features consist of physiological variables collected with different sampling rates at different points in time. Time-invariant features consist of patients’ demographics and score records. The performance is evaluated using cluster validity measures, showing that the proposed algorithm outperforms fuzzy c-means.]]>256177717941871<![CDATA[Fuzzy Regression Transfer Learning in Takagi–Sugeno Fuzzy Models]]>256179518071494<![CDATA[A Novel Approach to Reliable Output Feedback Control of Fuzzy-Affine Systems With Time Delays and Sensor Faults]]>$mathscr {H}_{infty }$ static output feedback control for nonlinear systems with time-varying delay and sensor faults in the piecewise-Markovian-Lyapunov-functional-based framework. The nonlinear plant is described by a continuous-time Takagi–Sugeno fuzzy-affine model with parametric uncertainties, and the sensor faults are characterized by a Markov process. Specifically, by applying a state-input augmentation technique, the original closed-loop system is first reformulated into a descriptor fuzzy-affine system. Based on a new piecewise-Markovian Lyapunov–Krasovskii functional, combined with a Wirtinger-based integral inequality, improved reciprocally convex inequality, and S-procedure, a novel bounded real lemma is then derived for the underlying closed-loop system. Furthermore, by taking advantage of the redundancy of descriptor system formulation, together with a linearization procedure, the piecewise-affine controller synthesis is carried out. It is shown that the desired piecewise-affine controller gains can be attained by solving a linear matrix inequality based optimization problem. Finally, simulation examples are performed to confirm the effectiveness and less conservatism of the presented approach.]]>25618081823735<![CDATA[Finite-Time Stabilization of a Class of T–S Fuzzy Systems]]>25618241829391<![CDATA[A Belief-Evolution-Based Approach for Online Control of Fuzzy Discrete-Event Systems Under Partial Observation]]>25618301836306<![CDATA[IEEE Computational Intelligence Society]]>256C3C344