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TOC Alert for Publication# 6221036 2018September17<![CDATA[Table of contents]]>4810C12773168<![CDATA[IEEE Transactions on Cybernetics]]>4810C2C2144<![CDATA[Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure]]>4810277427821116<![CDATA[Delay-Dependent Algebraic Riccati Equation to Stabilization of Networked Control Systems: Continuous-Time Case]]>${Q>0}$ and ${R>0}$ . In accordance with this result, we derive the Lyapunov/spectrum stabilizing criterion. Second, we apply the operator spectrum theory to study the stabilizing solution to a more general DARE with ${Qgeq 0}$ and ${R>0}$ . By defining a delay-dependent Lyapunov operator, we propose the existence theorem of the unique stabilizing solution. It is shown that the stabilizing solution, if it exists, is unique and coincides with a maximal solution. Third, as an application, we derive the explicit maximal allowable delay bound for a scalar system. To confirm the validity of our theoretic results, two illustrative examples are included in this paper.]]>481027832794583<![CDATA[Local-Density-Based Optimal Granulation and Manifold Information Granule Description]]>${O}({N})$ complexity, once the leading tree structure has been constructed. We describe IGs of arbitrary shapes using a small collection of landmark points positioned on the skeleton of the underlying manifold, which contribute to approximate reconstruction capabilities of the original dataset. A dissimilarity metric is developed to evaluate the quality of the obtained reconstruction. The interpretability of LoDOG IGs is discussed. Theoretical analysis and empirical evaluations are covered to demonstrate the effectiveness of LoDOG and the manifold description.]]>4810279528083049<![CDATA[NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints]]>Apriori property because the support ratio of a pattern may be greater than that of its subpatterns. Most importantly, patterns discovered by these methods are either too restrictive or too general and cannot represent underlying meaningful knowledge in the sequences. In this paper, we focus on a nonoverlapping sequence pattern mining task with gap constraints, where a nonoverlapping sequence pattern allows sequence letters to be flexibly and maximally utilized for pattern discovery. A new Apriori-based nonoverlapping sequence pattern mining algorithm, NOSEP, is proposed. NOSEP is a complete pattern mining algorithm, which uses a specially designed data structure, Nettree, to calculate the exact occurrence of a pattern in the sequence. Experimental results and comparisons on biology DNA sequences, time series data, and Gazelle datasets demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence patterns compared to other methods.]]>4810280928222254<![CDATA[Energy-to-Peak State Estimation for Static Neural Networks With Interval Time-Varying Delays]]>481028232835946<![CDATA[Marginally Stable Triangular Recurrent Neural Network Architecture for Time Series Prediction]]>${z}$ -plane by updating these weights based on the differential of the angular error variable. Such placement of the eigenvalues together with the extended close interaction between state variables facilitated by the nondiagonal triangular elements, enhances the learning ability of the proposed architecture. Simulation results show that the proposed architecture is highly effective in time-series prediction tasks associated with nonlinear and chaotic dynamic systems with underlying oscillatory modes. This modular architecture with dual upper and lower triangular feedback weight matrices mimics fully recurrent network architectures, while maintaining learning stability with a simplified training process. While training, the block-diagonal weights (hence the eigenvalues) of the dual triangular matrices are constrained to the same values during weight updates aimed at minimizing the possibility of overfitting. The dual triangular architecture also exploits the benefit of parsing the input and selectively applying the parsed inputs to the two subnetworks to facilitate enhanced learning performance.]]>4810283628502169<![CDATA[Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data]]>${3} {cdot } { {10}}^{{{18}}}$ transactions and more than 5 million of distinct single-items. The experimental stage includes comparisons against highly efficient and well-known pattern mining algorithms. Results reveal the interest of applying MapReduce versions when complex problems are considered, and also the unsuitability of this paradigm when dealing with small data.]]>4810285128651872<![CDATA[On Some Separated Algorithms for Separable Nonlinear Least Squares Problems]]>et al. respectively. The fourth one only uses the gradient of the reduced problem. Monte Carlo experiments are conducted to compare the performance of these four algorithms. From the results of the experiments, we find that: 1) the simplified Jacobian proposed by Ruano et al. is not a good choice for the VP algorithm; moreover, it may render the algorithm hard to converge; 2) the fourth algorithm perform moderately among these four algorithms; 3) the VP algorithm with the full Jacobian matrix perform more stable than that of the VP algorithm with Kuafman’s simplified one; and 4) the combination of VP algorithm and Levenberg–Marquardt method is more effective than the combination of VP algorithm and Gauss–Newton method.]]>4810286628741172<![CDATA[Discriminative Structured Dictionary Learning on Grassmann Manifolds and Its Application on Image Restoration]]>4810287528863274<![CDATA[Graph Learning for Multiview Clustering]]>$k$ -means clustering. Experiments are conducted on several benchmark datasets to verify the effectiveness and superiority of the proposed graph learning-based multiview clustering algorithm comparing to the state-of-the-art methods.]]>4810288728951466<![CDATA[Static and Dynamic Synthesis of Bengali and Devanagari Signatures]]>4810289629073353<![CDATA[Transductive Zero-Shot Learning With a Self-Training Dictionary Approach]]>4810290829192025<![CDATA[Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics]]>formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs’ heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.]]>4810292029341388<![CDATA[GII Representation-Based Cross-View Gait Recognition by Discriminative Projection With List-Wise Constraints]]>4810293529471766<![CDATA[Data-Driven Finite-Horizon Approximate Optimal Control for Discrete-Time Nonlinear Systems Using Iterative HDP Approach]]>4810294829611157<![CDATA[Adaptive Output Regulation of Heterogeneous Multiagent Systems Under Markovian Switching Topologies]]>4810296229711093<![CDATA[Cooperative Fault Tolerant Tracking Control for Multiagent Systems: An Intermediate Estimator-Based Approach]]>4810297229801580<![CDATA[A Generalized Methodology for Data Analysis]]>a priori. The typicality is expressed in a closed analytical form that can be calculated recursively and, thus, is computationally very efficient. The proposed nonparametric estimators of the ensemble properties of the data can also be interpreted as a discrete form of the information potential (known from the information theoretic learning theory as well as the Parzen windows). Therefore, EDA is very suitable for the current move to a data-rich environment, where the understanding of the underlying phenomena behind the available vast amounts of data is often not clear. We also present an extension of EDA for inference. The areas of applications of the new methodology of the EDA are wide because it concerns the very foundation of data analysis. Preliminary tests show its good performance in comparison to traditional techniques.]]>4810298129931075<![CDATA[Asymmetric Game: A Silver Bullet to Weighted Vertex Cover of Networks]]>4810299430051859<![CDATA[Person Reidentification via Discrepancy Matrix and Matrix Metric]]>4810300630202104<![CDATA[Finite-Time Synchronization of Networks via Quantized Intermittent Pinning Control]]>481030213027708<![CDATA[Introducing IEEE Collabratec]]>4810302830282056<![CDATA[IEEE Transactions on Cybernetics]]>4810C3C3165<![CDATA[IEEE Transactions on Cybernetics]]>4810C4C497