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IEEE Transactions on Cybernetics

Mingming Li, Shuzhi Sam Ge, Tong Heng Lee
This paper presents a novel content-driven associative memory (CDAM) to associate large-scale color images based on the subjects that represent the images' content. Compared to traditional associative memories, CDAM inherits their tolerance to random noise in images and possesses greater robustness against correlated noise that distorts an image's spatial contextual structure. A three-layer recurrent neural tensor network (RNTN) is designed as the network model of CDAM. Multiple salient objects detection algorithm and partial radial basis function (PRBF) kernel are proposed for subject determination and content-driven association, respectively...
November 29, 2016: IEEE Transactions on Cybernetics
Yong Zhang, Meng Joo Er, Rui Zhao, Mahardhika Pratama
Multidocument summarization has gained popularity in many real world applications because vital information can be extracted within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences and the ranking results rely heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features for sentence representation. In this paper, we leverage on word embedding to represent sentences so as to avoid the intensive labor in feature engineering...
November 28, 2016: IEEE Transactions on Cybernetics
Ci Chen, Zhi Liu, Kan Xie, Yun Zhang, C L Philip Chen
Most existing control results for pure-feedback stochastic systems are limited to a condition that tracking errors are bounded in probability. Departing from such bounded results, this paper proposes an asymptotic fuzzy neural network control for pure-feedback stochastic systems. The control goal is realized by proposing a novel semi-Nussbaum function-based technique and employing it in adaptive backstepping controller design. The proposed Nussbaum function is integrated with adaptive control technique to guarantee that the tracking error is asymptotically stable in probability...
November 28, 2016: IEEE Transactions on Cybernetics
Arnab Maity, Leonhard Hocht, Christian Heise, Florian Holzapfel
A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law...
November 28, 2016: IEEE Transactions on Cybernetics
Peng-Bo Zhang, Zhi-Xin Yang
The AdaBoost algorithm is a popular ensemble method that combines several weak learners to boost generalization performance. However, conventional AdaBoost.RT algorithms suffer from the limitation that the threshold value must be manually specified rather than chosen through a self-adaptive mechanism, which cannot guarantee a result in an optimal model for general cases. In this paper, we present a generic AdaBoost framework with robust threshold mechanism and structural optimization on regression problems...
November 24, 2016: IEEE Transactions on Cybernetics
Tianyu Wang, Zhongyang Han, Jun Zhao, Wei Wang
The flow variation tendency of byproduct gas plays a crucial role for energy scheduling in steel industry. An accurate prediction of its future trends will be significantly beneficial for the economic profits of steel enterprise. In this paper, a long-term prediction model for the energy system is proposed by providing an adaptive granulation-based method that considers the production semantics involved in the fluctuation tendency of the energy data, and partitions them into a series of information granules...
November 23, 2016: IEEE Transactions on Cybernetics
Myeong-Jin Park, Seung-Hoon Lee, Oh-Min Kwon, Ju H Park, Seong-Gon Choi
This paper designs a new leader-following consensus protocol for second-order multiagent systems with time-varying sampling. For the first time in designing a leader-following protocol, the concept of betweenness centrality is adopted to analyze the information flow in the consensus problem for multiagent systems. By construction of a suitable Lyapunov-Krasovskii functional, some criteria for designing consensus protocols of such systems are established in terms of linear matrix inequalities which can be easily solved by various effective optimization algorithms...
November 22, 2016: IEEE Transactions on Cybernetics
Shuai Shao, Hao Yang, Bin Jiang, Shuyao Cheng
This paper proposes a decentralized fault tolerant methodology for a class of interconnected nonlinear systems. The key novelty of our proposed method is that fault tolerant control can be achieved without necessarily exchanging the state information between the subsystems and the couplings' effect can be dealt with utilizing the cyclic-small-gain methodology. Simulation results demonstrate effectively the validity of our proposed approach.
November 22, 2016: IEEE Transactions on Cybernetics
Wenwu Yu, He Wang, Fei Cheng, Xinghuo Yu, Guanghui Wen
In this paper, the new decoupled distributed sliding-mode control (DSMC) is first proposed for second-order consensus in multiagent systems, which finally solves the fundamental unknown problem for sliding-mode control (SMC) design of coupled networked systems. A distributed full-order sliding-mode surface is designed based on the homogeneity with dilation for reaching second-order consensus in multiagent systems, under which the sliding-mode states are decoupled. Then, the SMC is applied to the decoupled sliding-mode states to reach their origin in finite time, which is the sliding-mode surface...
November 22, 2016: IEEE Transactions on Cybernetics
Biao Luo, Derong Liu, Huai-Ning Wu, Ding Wang, Frank L Lewis
The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed.
November 22, 2016: IEEE Transactions on Cybernetics
Chunguang Li, Songyan Huang, Ying Liu, Zhaoyang Zhang
Distributed data processing over networks has received a lot of attention due to its wide applicability. In this paper, we consider the multitask problem of in-network distributed estimation. For the multitask problem, the unknown parameter vectors (tasks) for different nodes can be different. Moreover, considering some real application scenarios, it is also assumed that there are some similarities among the tasks. Thus, the intertask cooperation is helpful to enhance the estimation performance. In this paper, we exploit an additional special characteristic of the vectors of interest, namely, joint sparsity, aiming to further enhance the estimation performance...
November 18, 2016: IEEE Transactions on Cybernetics
Dawei Du, Honggang Qi, Longyin Wen, Qi Tian, Qingming Huang, Siwei Lyu
Graph-based representation is widely used in visual tracking field by finding correct correspondences between target parts in different frames. However, most graph-based trackers consider pairwise geometric relations between local parts. They do not make full use of the target's intrinsic structure, thereby making the representation easily disturbed by errors in pairwise affinities when large deformation or occlusion occurs. In this paper, we propose a geometric hypergraph learning-based tracking method, which fully exploits high-order geometric relations among multiple correspondences of parts in different frames...
November 18, 2016: IEEE Transactions on Cybernetics
Ding Tu, Ling Chen, Xiaokang Yu, Gencai Chen
Rare category detection aims to find interesting and statistically significant anomalies and incorporates ideas from active learning and semisupervised learning. The challenge of rare category detection is to find the rare classes of the anomalies in a data set where the data distribution is skewed. Most existing rare category detection methods suppose that the user knows the specific number of all classes in advance, which cannot be satisfied in most real scenarios. In this paper, we propose a new rare category detection framework composed of active learning and semisupervised hierarchical density-based clustering...
November 17, 2016: IEEE Transactions on Cybernetics
Anima Majumder, Laxmidhar Behera, Venkatesh K Subramanian
This paper presents a novel automatic facial expressions recognition system (AFERS) using the deep network framework. The proposed AFERS consists of four steps: 1) geometric features extraction; 2) regional local binary pattern (LBP) features extraction; 3) fusion of both the features using autoencoders; and 4) classification using Kohonen self-organizing map (SOM)-based classifier. This paper makes three distinct contributions. The proposed deep network consisting of autoencoders and the SOM-based classifier is computationally more efficient and performance wise more accurate...
November 17, 2016: IEEE Transactions on Cybernetics
Xianbin Cao, Xiaolong Jiang, Xiaomei Li, Pingkun Yan
Visual target tracking is one of the most important research areas in the field of computer vision. Within this realm, multiple targets tracking (MTT) under complicated scene stands out for its great availability in real life applications, such as urban traffic surveillance and sports video analysis. However, in MTT, main difficulties arise from large variation in target saliency and significant motion heterogeneity, which may result in the failure of tracking weak targets. To tackle this challenge, a novel hierarchical layered tracking structure is proposed to perform tracking sequentially layer-by-layer...
November 17, 2016: IEEE Transactions on Cybernetics
Ping Li, Jun Yu, Meng Wang, Luming Zhang, Deng Cai, Xuelong Li
Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization. Most low-rank methods are incapable of capturing low-dimensional subspace for supervised learning tasks, e.g., classification and regression. This paper aims to learn both the discriminant low-rank representation (LRR) and the robust projecting subspace in a supervised manner. To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization...
November 10, 2016: IEEE Transactions on Cybernetics
Nikolaos Passalis, Anastasios Tefas
In this paper, a manifold-based dictionary learning method for the bag-of-features (BoF) representation optimized toward information clustering is proposed. First, the spectral representation, which unwraps the manifolds of the data and provides better clustering solutions, is formed. Then, a new dictionary is learned in order to make the histogram space, i.e., the space where the BoF historgrams exist, as similar as possible to the spectral space. The ability of the proposed method to improve the clustering solutions is demonstrated using a wide range of datasets: two image datasets, the 15-scene dataset and the Corel image dataset, one video dataset, the KTH dataset, and one text dataset, the RT-2k dataset...
November 10, 2016: IEEE Transactions on Cybernetics
Ke Li, Kalyanmoy Deb, Qingfu Zhang, Qiang Zhang
Nondominated sorting (NDS), which divides a population into several nondomination levels (NDLs), is a basic step in many evolutionary multiobjective optimization (EMO) algorithms. It has been widely studied in a generational evolution model, where the environmental selection is performed after generating a whole population of offspring. However, in a steady-state evolution model, where a population is updated right after the generation of a new candidate, the NDS can be extremely time consuming. This is especially severe when the number of objectives and population size become large...
November 8, 2016: IEEE Transactions on Cybernetics
Alvaro Rubio-Largo, Leonardo Vanneschi, Mauro Castelli, Miguel A Vega-Rodriguez
The multiple sequence alignment is a well-known bioinformatics problem that consists in the alignment of three or more biological sequences (protein or nucleic acid). In the literature, a number of tools have been proposed for dealing with this biological sequence alignment problem, such as progressive methods, consistency-based methods, or iterative methods; among others. These aligners often use a default parameter configuration for all the input sequences to align. However, the default configuration is not always the best choice, the alignment accuracy of the tool may be highly boosted if specific parameter configurations are used, depending on the biological characteristics of the input sequences...
November 2, 2016: IEEE Transactions on Cybernetics
Jan Skach, Bahare Kiumarsi, Frank L Lewis, Ondrej Straka
In this paper, motivated by human neurocognitive experiments, a model-free off-policy reinforcement learning algorithm is developed to solve the optimal tracking control of multiple-model linear discrete-time systems. First, an adaptive self-organizing map neural network is used to determine the system behavior from measured data and to assign a responsibility signal to each of system possible behaviors. A new model is added if a sudden change of system behavior is detected from the measured data and the behavior has not been previously detected...
November 2, 2016: IEEE Transactions on Cybernetics
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