<![CDATA[ IEEE Transactions on Fuzzy Systems - new TOC ]]>
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TOC Alert for Publication# 91 2017January 19<![CDATA[Table of Contents]]>246C1C448<![CDATA[IEEE Transactions on Fuzzy Systems]]>246C2C264<![CDATA[Editorial]]>24612571258116<![CDATA[Switched Fuzzy Output Feedback Control and Its Application to a Mass–Spring–Damping System]]>24612591269850<![CDATA[Intuitionistic Fuzzy Time Series: An Approach for Handling Nondeterminism in Time Series Forecasting]]>24612701281461<![CDATA[Output-Feedback Based Sliding Mode Control for Fuzzy Systems With Actuator Saturation]]>246128212931731<![CDATA[Online Feature Selection Based on Fuzzy Clustering and Its Applications]]>246129413061204<![CDATA[A Big Bang–Big Crunch Type-2 Fuzzy Logic System for Machine-Vision-Based Event Detection and Summarization in Real-World Ambient-Assisted Living]]>246130713191003<![CDATA[Weighted Fuzzy Observer-Based Fault Detection Approach for Discrete-Time Nonlinear Systems via Piecewise-Fuzzy Lyapunov Functions]]> $mathcal {L}_2$ observer-based fault detection (FD) systems for discrete-time nonlinear industrial processes. To gain a deeper insight into this FD framework, the existence condition is introduced first. Then, an integrated design of $mathcal {L}_2$ observer-based FD approach is realized by solving the proposed existence condition with the aid of Takagi–Sugeno fuzzy dynamic modeling technique and piecewise-fuzzy Lyapunov functions. Most importantly, a weighted piecewise-fuzzy observer-based residual generator is proposed, aiming at achieving an optimal integration of residual evaluation and threshold computation into FD systems. The core of this approach is to make use of the knowledge provided by fuzzy models of each local region and then to weight the local residual signal by means of different weighting factors. In comparison with the standard norm-based fuzzy observer-based FD methods, the proposed scheme may lead to a significant improvement of the FD performance. In the end, the effectiveness of the proposed method is verified by a numerical example and a case study on the laboratory setup of continuous stirred tank heater plant.]]>246132013331344<![CDATA[Disturbance Rejection Fuzzy Control for Nonlinear Parabolic PDE Systems via Multiple Observers]]>246133413481079<![CDATA[Processing Incomplete <italic>k</italic> Nearest Neighbor Search]]>S of multidimensional objects and a query object q, a k nearest neighbor (kNN) query finds from S the k closest objects to q. This query is a fundamental problem in database, data mining, and information retrieval research. It plays an important role in a wide spectrum of real applications such as image recognition and location-based services. However, due to the failure of data transmission devices, improper storage, and accidental loss, incomplete data exist widely in those applications, where some dimensional values of data items are missing. In this paper, we systematically study incomplete k nearest neighbor (I kNN) search, which aims at the kNN query for incomplete data. We formalize this problem and propose an efficient lattice partition algorithm using our newly developed $Lalpha B$ index to support exact IkNN retrieval, with the help of two pruning heuristics, i.e., $alpha $value pruning and partial distance pruning. Furthermore, we propose an approximate algorithm, namely histogram approximate , to support approximate IkNN search with improved search efficiency and guaranteed error bound. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of newly designed indexes and pruning heuristics, as well as the performance of our presented algorithms under a variety of experimental settings.]]>246134913632035<![CDATA[Online Local Input Selection Through Evolving Heterogeneous Fuzzy Inference System]]>24613641377983<![CDATA[Low Design-Cost Fuzzy Controllers for Robust Stabilization of Nonlinear Partial Differential Systems]]>∞ stabilization design is developed for a class of N-dimensional nonlinear parabolic PDSs. Further, two low design-cost robust fuzzy controllers, called the robust fuzzy area-controller and point-controller, as well as a normal design-cost robust fuzzy full-controller are proposed for this problem. The difference between the three control designs lies in their controller placement in the spatial domain. First, we present the N-dimensional parabolic Takagi-Sugeno (T-S) fuzzy PDS based on the knowledge-based fuzzy system technique. Next, these three robust fuzzy controllers are constructed via solving diffusion matrix inequality (DMI) problems. With the proposed simple but general method using the Poincareinequality, the linear matrix inequality problems are provided to replace DMI problems for the robust fuzzy H_{∞} stabilization designs for computational simplicity. Further, the comparison of these three robust fuzzy controllers are demonstrated to enable a designer to select a low-cost option. Finally, a simulation example is provided to illustrate the design procedure and verify the performance of the proposed designs.]]>246137813941080<![CDATA[Fuzzy Multi-Instance Classifiers]]>24613951409518<![CDATA[Relationships Between Two Types of Intuitionistic Fuzzy Definite Integrals]]>246141014251502<![CDATA[Adaptive Fuzzy Output Feedback Control for Switched Nonstrict-Feedback Nonlinear Systems With Input Nonlinearities]]>246142614401426<![CDATA[Adaptive Fuzzy Tracking Control Design for SISO Uncertain Nonstrict Feedback Nonlinear Systems]]>246144114541125<![CDATA[Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System]]>246145514631237<![CDATA[Finite-Frequency Model Reduction of Takagi–Sugeno Fuzzy Systems]]>∞ performance index is first defined. Then, a sufficient finite-frequency performance analysis condition is derived by the aid of Parseval's theorem and quadratic functions. Based on this condition and projection lemma, three model-reduction algorithms for T-S fuzzy systems with input signals in low-frequency, middle-frequency, and high-frequency domain are obtained, respectively. Finally, an example is given to illustrate the effectiveness of the proposed method.]]>24614641474571<![CDATA[Monotonicity of SISO Fuzzy Relational Inference With an Implicative Rule Base]]>$text{[0, 1];}$ and 2) the original rule base is employed without any alteration. We determine conditions under which monotonicity of an FRI, where the rule base is modeled by a strict fuzzy implication, can be ensured without transforming the original rule base. Thus, the results in this work further augment the case for considering fuzzy implications, other than those from the residuated setting, to be used in applications.]]>24614751487779<![CDATA[Finding Synergy Networks From Gene Expression Data: A Fuzzy-Rule-Based Approach]]>246148814991046<![CDATA[The Generalized C Index for Internal Fuzzy Cluster Validity]]>24615001512834<![CDATA[Improved Uncertainty Capture for Nonsingleton Fuzzy Systems]]>246151315241365<![CDATA[On Generalized Extended Bonferroni Means for Decision Making]]>24615251543805<![CDATA[Robust ${H_infty }$-Based Synchronization of the Fractional-Order Chaotic Systems by Using New Self-Evolving Nonsingleton Type-2 Fuzzy Neural Networks]]>∞-based adaptive fuzzy control is presented for the synchronization of fractional-order chaotic systems. A self-evolving nonsingleton type-2 fuzzy neural network (SE-NST2FNN) is proposed for the estimation of the unknown functions in the dynamics of the system. The effects of the approximation error and the external disturbances are eliminated by designing an adaptive compensator, such that the H_{∞} norm of the synchronization error is minimized and asymptotically stability is achieved. The consequent parameters of SE-NST2FNN are tuned based on the adaptation laws that are derived from Lyapunov stability analysis. The antecedent part and the rule database of SE-NST2FNN are optimized based on a clustering method and the modified invasive weed optimization algorithm, respectively. The effectiveness of proposed control scheme is verified by simulation examples.]]>246154415542766<![CDATA[Dissimilarity Metric Learning in the Belief Function Framework]]>24615551564884<![CDATA[A Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System for Handling Nonstationarities in EEG Signals for BCI]]>24615651577699<![CDATA[Uncertain Calculus With Yao Process]]>24615781585291<![CDATA[Interval-Valued Atanassov Intuitionistic OWA Aggregations Using Admissible Linear Orders and Their Application to Decision Making]]>246158615971053<![CDATA[Dempster–Shafer Fusion of Evidential Pairwise Markov Chains]]>24615981610598<![CDATA[Sparsity-Aware Possibilistic Clustering Algorithms]]>246161116262624<![CDATA[Mean-Semi-Entropy Models of Fuzzy Portfolio Selection]]>24616271636557<![CDATA[Uncertain Random Renewal Reward Process With Application to Block Replacement Policy]]>24616371647463<![CDATA[A Note on Fuzzy Joint Points Clustering Methods for Large Datasets]]>24616481653388<![CDATA[Sampled-Data Fuzzy Stabilization of Nonlinear Systems Under Nonuniform Sampling]]>24616541667657<![CDATA[H-Index and Other Sugeno Integrals: Some Defects and Their Compensation]]>24616681672338<![CDATA[On Monotonicity of Takagi–Sugeno Fuzzy Systems With Ellipsoidal Regions]]>24616731678791<![CDATA[Nonfragile Fault-Tolerant Fuzzy Observer-Based Controller Design for Nonlinear Systems]]>24616791689656<![CDATA[Generators of Aggregation Functions and Fuzzy Connectives]]>$[0,1]$ can be generated as a composition of infinitary sup-operation $bigvee$ acting on sets with cardinality not exceeding $mathfrak {c}$, $b$-medians $mathsf {Med}_b$, $bin [0,1[$, and unary aggregation functions $1_{]0,1]}$ and $1_{[a,1]}$, $ain, ]0,1]$. Moreover, we show that we cannot relax the cardinality of argument sets for suprema to be countable, thus showing a kind of minimality of the introduced generating set. As a by-product, generating sets for fuzzy connectives, such as fuzzy unions, fuzzy intersections, and fuzzy implications, are obtained, too.]]>24616901694403<![CDATA[Introducing IEEE Collabratec]]>246169516951856<![CDATA[Become a published author in 4 to 6 weeks]]>24616961696790<![CDATA[IEEE Computational Intelligence Society]]>246C3C357