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TOC Alert for Publication# 91 2019April 18<![CDATA[Table of Contents]]>274C1C146<![CDATA[IEEE Transactions on Fuzzy Systems]]>274C2C267<![CDATA[General 3-D Type-II Fuzzy Logic Systems in the Polar Frame: Concept and Practice]]>^{1}

Because many acronyms are used in this paper, they are summarized in Table I for the convenience of the reader.

We focused on the automatic membership function (MF) Generator, general type-2 polar fuzzy membership, new geometric operators in polar frame, inference consisting of fuzzy 3-D polar rules and antecedents/consequents $theta$-slice and $alpha$-planes. The cubic smoothing spline is introduced to generate the upper and lower MFs according to the information theory. Three indices of compactness, smoothness, and entropy are employed for tuning of MFs. A measure of ultrafuzziness, min, max, equal, and reduced ultrafuzziness are suggested for the polar method. Additionally, the set theoretic operations of 3-D type-2 fuzzy sets in the polar frame are discussed. We also prove the join, meet, centroid, and type-reduction operations in the polar frame. Several rule sets are given to show the usefulness and complexity of the proposed method. The performance of the partial general 3D polar type-2 fuzzy logic system showed linear growth as the number of rules was increased. Computation time tests showed that the algorithm reduces the computation time by a maximum of 67% compared with discrete MF and is shown by an extreme 98% for the geometric procedure. These results indicate significant improvements in computation time for the spline interpolation over the existing methods.]]>2746216345255<![CDATA[Survey of Fuzzy Min–Max Neural Network for Pattern Classification Variants and Applications]]>2746356451811<![CDATA[A Novel Finite-Time Adaptive Fuzzy Tracking Control Scheme for Nonstrict Feedback Systems]]>2746466581177<![CDATA[Dissipativity-Preserving Model Reduction for Takagi–Sugeno Fuzzy Systems]]>$H_{infty }$ performance index is used to describe the approximation error. Meanwhile, dissipativity of the reduced-order model is guaranteed by satisfying a dissipation inequality. With the aid of fuzzy-basis-dependent Lyapunov functions and slack variable techniques, less conservative design conditions for reduced-order models are derived. An algorithm is proposed to calculate a desired reduced-order model. A rail traction control system is given to illustrate the effectiveness of the proposed method and the advantages over the existing methods.]]>274659670644<![CDATA[Quality Control Process Based on Fuzzy Random Variables]]>2746716851439<![CDATA[Multiobjective <inline-formula><tex-math notation="LaTeX">$H_{2}/H_{infty }$</tex-math></inline-formula> Control Design of the Nonlinear Mean-Field Stochastic Jump-Diffusion Systems via Fuzzy Approach]]>$H_{2}/H_{infty }$ fuzzy control design is investigated for nonlinear mean-field jump diffusion (MFSJD) systems for concurrently minimizing both $H_{2}$ and $H_{infty }$ performance. Since $H_{2}$ and $H_{infty }$ performance usually conflict with one another, the optimization problem that concurrently minimizes $H_{2}$ and $H_{infty }$ performance can be regarded as a dynamically constrained multiobjective optimization problem (MOP). Because Hamilton–Jacobi inequalities of the nonlinear MFSJD systems are difficult to derive, multiobjective $H_{2}/H_{infty }$ control design problems of nonlinear MFSJD system are difficult to solve. The Takagi–Sugeno fuzzy interpolation scheme and an indirect method are introduced to help transform the dynamically constrained MOP into linear matrix inequalities (LMIs) constrained MOP. Thus, one can accomplish the multiobjective $H_{2}/H_{infty }$ fuzzy control design via LMI-constrained multiobjective evolutionary algorithms (MOEAs). To efficiently solve the multiobjective $H_{2}/H_{infty }$ control design problem, we propose a novel LMI-constrained MOEA called fronts-squeezing. The fronts-squeezing LMI-constrained MOEA can concurrently search Pareto front fr-
m both sides of feasible and infeasible regions and narrow the search region down to increase efficiency. Finally, we present a simulation example about the multiobjective regulation of nonlinear MFSJD financial system to illustrate the design procedure and verify the proposed theories.]]>2746867001148<![CDATA[A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity]]>2747017153831<![CDATA[Additive Consistency of Hesitant Fuzzy Linguistic Preference Relation With a New Expansion Principle for Hesitant Fuzzy Linguistic Term Sets]]>274716730727<![CDATA[A Method of Measuring Uncertainty for Z-Number]]>274731738356<![CDATA[A Weighted Least Squares Fuzzy Regression for Crisp Input-Fuzzy Output Data]]>2747397482520<![CDATA[An SOS-Based Sliding Mode Controller Design for a Class of Polynomial Fuzzy Systems]]>$mathbf {B}_i(x)$ of the considered system is not equal to one another. To solve this problem, first, we rewrite every column vector of input matrix $mathbf {B}_i(x)$ using the vectors of the basis matrix $mathbb {B}(x)$. Second, by use of the column vector of $mathbb {B}(x)$, the subsliding mode surface $s_p(t)$ is designed. Based on the rewritten system, a novel sliding mode surface $s(t)$ is devised. The sliding mode surface parameter matrix can be characterized in terms of the solution of the provided sum of squares conditions. Then, a sliding mode controller is proposed to stabilize the considered system. It could make the state of the considered system drive onto $s(t)$ and avoid the state escaping from the surface in the subsequent time. A lemma is provided to obtain the final result. Finally, practical and numerical examples are provided to demonstrate the validity of the proposed approach.]]>274749759753<![CDATA[Fuzzy Pushdown Termination Games]]>274760774848<![CDATA[Granular Fuzzy Modeling for Multidimensional Numeric Data: A Layered Approach Based on Hyperbox]]>2747757895269<![CDATA[Decentralized Dissipative Filtering for Delayed Nonlinear Interconnected Systems Based on T–S Fuzzy Model]]>$(Q, S,R)-alpha -$ dissipativity. In addition, a suitable filter is designed by solving a set of linear matrix inequalities. The presented method can provide better performance than the existing ones for the case of $mathcal {H}_infty$ filtering. A simulation example is given to demonstrate the validity of the developed filter design technique.]]>274790801661<![CDATA[Delayed Fuzzy Control of a 1-D Reaction-Diffusion Equation Using Sampled-in-Space Sensing and Actuation]]>2748028091422<![CDATA[Supervised Learning to Aggregate Data With the Sugeno Integral]]>h-indices to bibliometric data.]]>274810815800<![CDATA[Interval Observer Design for Discrete-Time Uncertain Takagi–Sugeno Fuzzy Systems]]>$L_{infty }$ norm-based approach is used in the design of interval observer to attenuate the effect of the unknown disturbances, noise, and parametric uncertainty. Furthermore, the design conditions are formulated into a set of linear matrix inequalities, which can be efficiently solved. Numerical simulations are given to illustrate the effectiveness of the proposed method.]]>274816823599<![CDATA[Characterization of Quadratic Aggregation Functions]]>274824829180<![CDATA[Introducing IEEE Collabratec]]>2748308301857<![CDATA[IEEE Computational Intelligence Society]]>274C3C359<![CDATA[Information for authors]]>274C4C483