<![CDATA[ IEEE Transactions on Cybernetics - new TOC ]]>
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TOC Alert for Publication# 6221036 2017January 16<![CDATA[Table of contents]]>472C1269169<![CDATA[IEEE Transactions on Cybernetics]]>472C2C273<![CDATA[Norm Monitoring Under Partial Action Observability]]>472270282812<![CDATA[Real-Time Fault Detection Approach for Nonlinear Systems and its Asynchronous T–S Fuzzy Observer-Based Implementation]]>${mathcal {L}}_{ {infty } }/ {mathcal {L}}_{ {2}}$ type of nonlinear observer-based FD systems. This analytical framework is fundamental for the development of real-time nonlinear FD systems with the aid of some well-established techniques. In the second part, we address the integrated design of the $ {mathcal {L}}_{ {infty } }/ {mathcal {L}}_{ {2}}$ observer-based FD systems by applying Takagi–Sugeno (T–S) fuzzy dynamic modeling technique as the solution tool. This fuzzy observer-based FD approach is developed via piecewise Lyapunov functions, and can be applied to the case that the premise variables of the FD system is nonsynchronous with the premise variables of the fuzzy model of the plant. In the end, a case study on the laboratory setup of three-tank system is given to show the efficiency of the proposed results.]]>472283294798<![CDATA[Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics]]>472295305665<![CDATA[Adjustable Parameter-Based Distributed Fault Estimation Observer Design for Multiagent Systems With Directed Graphs]]>${H} _{{infty }}$ and ${H} _{{2}}$ with pole placement, multiconstrained design is given to calculate the gain of DFEO. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed DFEO design with AP.]]>472306314883<![CDATA[Rate and Distortion Optimization for Reversible Data Hiding Using Multiple Histogram Shifting]]>4723153261522<![CDATA[Leader-Following Consensus of Nonlinear Multiagent Systems With Stochastic Sampling]]>4723273387189<![CDATA[An Incremental Type-2 Meta-Cognitive Extreme Learning Machine]]>4723393531614<![CDATA[Robust Object Tracking via Key Patch Sparse Representation]]>4723543642406<![CDATA[$H_infty $ Control for 2-D Fuzzy Systems With Interval Time-Varying Delays and Missing Measurements]]>${H_{infty }}$ control problem for a class of 2-D Takagi–Sugeno fuzzy described by the second Fornasini–Machesini local state-space model with time-delays and missing measurements. The state delays are allowed to be time-varying within a known interval. The measurement output is subject to randomly intermittent packet dropouts governed by a random sequence satisfying the Bernoulli distribution. The purpose of the addressed problem is to design an output-feedback controller such that the closed-loop system is globally asymptotically stable in the mean square and the prescribed ${H_infty }$ performance index is satisfied. By employing a combination of the intensive stochastic analysis and the free weighting matrix method, several delay-range-dependent sufficient conditions are presented that guarantee the existence of the desired controllers for all possible time-delays and missing measurements. The explicit expressions of such controllers are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. Finally, a numerical simulation example is given to demonstrate the applicability of the proposed control scheme.]]>4723653771041<![CDATA[Superimposed Sparse Parameter Classifiers for Face Recognition]]>4723783903294<![CDATA[A Nonhomogeneous Cuckoo Search Algorithm Based on Quantum Mechanism for Real Parameter Optimization]]>4723914021920<![CDATA[Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics]]>472403414728<![CDATA[Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data]]>472415424997<![CDATA[Stochastic Optimal Regulation of Nonlinear Networked Control Systems by Using Event-Driven Adaptive Dynamic Programming]]>472425438680<![CDATA[Latent Max-Margin Multitask Learning With Skelets for 3-D Action Recognition]]>4724394481431<![CDATA[Cross-Modal Retrieval With CNN Visual Features: A New Baseline]]>4724494602541<![CDATA[A Benchmark Test Suite for Dynamic Evolutionary Multiobjective Optimization]]>4724614721446<![CDATA[A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems]]>4724734841871<![CDATA[Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning]]>${ell }_{1,2}$ mixed-norm, which can not only ensures the local consistency of target appearance, but also tolerates the sudden changes between two adjacent frames; and 3) to alleviate the inference of unreasonable representation values due to outlier candidates, an adaptive weighted scheme is designed to improve the robustness of the tracker. By evaluating on 26 challenge video sequences, the experiments show the effectiveness and favorable performance of the proposed algorithm against 12 state-of-the-art visual trackers.]]>4724854982325<![CDATA[Classifying a Person’s Degree of Accessibility From Natural Body Language During Social Human–Robot Interactions]]>4725245382927<![CDATA[A Two-Phase Multiobjective Evolutionary Algorithm for Enhancing the Robustness of Scale-Free Networks Against Multiple Malicious Attacks]]>MMA. In MOEA-RSF_{MMA}, a single-objective sampling phase is first used to generate a good initial population for the later two-objective optimization phase. Such a two-phase optimizing pattern well balances the computational cost of the two objectives and improves the search efficiency. In the experiments, both synthetic scale-free networks and real-world networks are used to validate the performance of MOEA-RSF_{MMA}. Moreover, both local and global characteristics of networks in different parts of the obtained Pareto fronts are studied. The results show that the networks in different parts of Pareto fronts reflect different properties, and provide various choices for decision makers.]]>4725395522291<![CDATA[IEEE Transactions on Cybernetics]]>472C3C3205<![CDATA[IEEE Transactions on Cybernetics]]>472C4C463