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IEEE Transactions on Cybernetics

Sanyou Zeng, Ruwang Jiao, Changhe Li, Xi Li, Jawdat S Alkasassbeh
A novel multiobjective technique is proposed for solving constrained optimization problems (COPs) in this paper. The method highlights three different perspectives: 1) a COP is converted into an equivalent dynamic constrained multiobjective optimization problem (DCMOP) with three objectives: a) the original objective; b) a constraint-violation objective; and c) a niche-count objective; 2) a method of gradually reducing the constraint boundary aims to handle the constraint difficulty; and 3) a method of gradually reducing the niche size aims to handle the multimodal difficulty...
January 16, 2017: IEEE Transactions on Cybernetics
Ben Niu, Yanjun Liu, Guangdeng Zong, Zhaoyu Han, Jun Fu
In this paper, a new adaptive approximation-based tracking controller design approach is developed for a class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing a novel barrier Lyapunov function (BLF), the constrained switched system is first transformed into a new system without any constraint, which means the control objectives of the both systems are equivalent. Then command filter technique is applied to solve the so-called "explosion of complexity" problem in traditional backstepping procedure, and radial basis function NNs are directly employed to model the unknown nonlinear functions...
January 16, 2017: IEEE Transactions on Cybernetics
Luyang Li, Yun-Hui Liu, Tianjiao Jiang, Kai Wang, Mu Fang
Despite tremendous efforts made for years, trajectory tracking control (TC) of a nonholonomic mobile robot (NMR) without global positioning system remains an open problem. The major reason is the difficulty to localize the robot by using its onboard sensors only. In this paper, a newly designed adaptive trajectory TC method is proposed for the NMR without its position, orientation, and velocity measurements. The controller is designed on the basis of a novel algorithm to estimate position and velocity of the robot online from visual feedback of an omnidirectional camera...
January 16, 2017: IEEE Transactions on Cybernetics
Baiying Lei, Peng Yang, Tianfu Wang, Siping Chen, Dong Ni
Accurate identification and understanding informative feature is important for early Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminative learning technique to expand the class-specific difference and include geometric information for effective feature selection. In addition, two kind of relational information are incorporated to explore the intrinsic relationships among features and training subjects in terms of similarity learning...
January 16, 2017: IEEE Transactions on Cybernetics
Ke Yan, Lu Kou, David Zhang
Domain adaptation algorithms are useful when the distributions of the training and the test data are different. In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space. We propose maximum independence domain adaptation (MIDA) and semi-supervised MIDA to address this problem. Domain features are first defined to describe the background information of a sample, such as the device label and acquisition time...
January 16, 2017: IEEE Transactions on Cybernetics
Yujia Zhao, Alireza Fatehi, Biao Huang
This paper is concerned with robust identification of processes with time-varying time delays. In reality, the delay values do not simply change randomly, but there is a correlation between consecutive delays. In this paper, the correlation of time delay is modeled by the transition probability of a Markov chain. Furthermore, the measured data are often contaminated by outliers, and therefore, t-distribution is adopted to model the measurement noise. The variational Bayesian (VB) approach is applied to estimate the model parameters along with time delays...
January 10, 2017: IEEE Transactions on Cybernetics
Wei Wang, Shaocheng Tong
This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains...
January 10, 2017: IEEE Transactions on Cybernetics
Huanqing Wang, Peter Xiaoping Liu, Shichao Liu
This paper considers the master and slave synchronization control for bilateral teleoperation systems with time delay and backlash-like hysteresis. Based on radial basis functions neural networks' approximation capabilities, two improved adaptive neural control approaches are developed. By Lyapunov stability analysis, the position and velocity tracking errors are guaranteed to converge to a small neighborhood of the origin. The contributions of this paper can be summarized as follows: 1) by using the matrix norm established using the weight vector of neural networks as the estimated parameters, two novel control schemes are developed and 2) the hysteresis inverse is not required in the proposed controllers...
January 10, 2017: IEEE Transactions on Cybernetics
Yuanheng Zhu, Dongbin Zhao, Xiong Yang, Qichao Zhang
Sum of squares (SOS) polynomials have provided a computationally tractable way to deal with inequality constraints appearing in many control problems. It can also act as an approximator in the framework of adaptive dynamic programming. In this paper, an approximate solution to the H∞ optimal control of polynomial nonlinear systems is proposed. Under a given attenuation coefficient, the Hamilton-Jacobi-Isaacs equation is relaxed to an optimization problem with a set of inequalities. After applying the policy iteration technique and constraining inequalities to SOS, the optimization problem is divided into a sequence of feasible semidefinite programming problems...
January 10, 2017: IEEE Transactions on Cybernetics
Yiping Liu, Dunwei Gong, Jing Sun, Yaochu Jin
Most existing multiobjective evolutionary algorithms experience difficulties in solving many-objective optimization problems due to their incapability to balance convergence and diversity in the high-dimensional objective space. In this paper, we propose a novel many-objective evolutionary algorithm using a one-by-one selection strategy. The main idea is that in the environmental selection, offspring individuals are selected one by one based on a computationally efficient convergence indicator to increase the selection pressure toward the Pareto optimal front...
January 9, 2017: IEEE Transactions on Cybernetics
Muhammad Yousefnezhad, Sheng-Jun Huang, Daoqiang Zhang
The wisdom of crowds (WOCs), as a theory in the social science, gets a new paradigm in computer science. The WOC theory explains that the aggregate decision made by a group is often better than those of its individual members if specific conditions are satisfied. This paper presents a novel framework for unsupervised and semisupervised cluster ensemble by exploiting the WOC theory. We employ four conditions in the WOC theory, i.e., diversity, independency, decentralization, and aggregation, to guide both constructing of individual clustering results and final combination for clustering ensemble...
January 4, 2017: IEEE Transactions on Cybernetics
Zhongyun Hua, Shuang Yi, Yicong Zhou, Chengqing Li, Yue Wu
Generating chaotic maps with expected dynamics of users is a challenging topic. Utilizing the inherent relation between the Lyapunov exponents (LEs) of the Cat map and its associated Cat matrix, this paper proposes a simple but efficient method to construct an n-dimensional (n-D) hyperchaotic Cat map (HCM) with any desired number of positive LEs. The method first generates two basic n-D Cat matrices iteratively and then constructs the final n-D Cat matrix by performing similarity transformation on one basic n-D Cat matrix by the other...
January 4, 2017: IEEE Transactions on Cybernetics
Shan Zuo, Yongduan Song, Frank L Lewis, Ali Davoudi
This paper studies the output containment control of linear heterogeneous multi-agent systems, where the system dynamics and even the state dimensions can generally be different. Since the states can have different dimensions, standard results from state containment control do not apply. Therefore, the control objective is to guarantee the convergence of the output of each follower to the dynamic convex hull spanned by the outputs of leaders. This can be achieved by making certain output containment errors go to zero asymptotically...
January 4, 2017: IEEE Transactions on Cybernetics
Aizhu Zhang, Genyun Sun, Jinchang Ren, Xiaodong Li, Zhenjie Wang, Xiuping Jia
Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named Kbest, which stores those superior agents after fitness sorting in each iteration. Since the global property of Kbest remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence...
December 30, 2016: IEEE Transactions on Cybernetics
Peijia Ren, Zeshui Xu, Zhinan Hao
Due to the timeliness of emergency response and much unknown information in emergency situations, this paper proposes a method to deal with the emergency decision making, which can comprehensively reflect the emergency decision making process. By utilizing the hesitant fuzzy elements to represent the fuzziness of the objects and the hesitant thought of the experts, this paper introduces the negative exponential function into the prospect theory so as to portray the psychological behaviors of the experts, which transforms the hesitant fuzzy decision matrix into the hesitant fuzzy prospect decision matrix (HFPDM) according to the expectation-levels...
December 30, 2016: IEEE Transactions on Cybernetics
Jen-Wei Yeh, Shun-Feng Su
This paper presents an efficient approach for the use of recursive least square (RLS) learning algorithm in Takagi-Sugeno-Kang neural fuzzy systems. In the use of RLS, reduced covariance matrix, of which the off-diagonal blocks defining the correlation between rules are set to zeros, may be employed to reduce computational burden. However, as reported in the literature, the performance of such an approach is slightly worse than that of using the full covariance matrix. In this paper, we proposed a so-called enhanced local learning concept in which a threshold is considered to stop learning for those less fired rules...
December 29, 2016: IEEE Transactions on Cybernetics
Jin Xie, Guoxian Dai, Fan Zhu, Ling Shao, Yi Fang
Effective 3-D shape retrieval is an important problem in 3-D shape analysis. Recently, feature learning-based shape retrieval methods have been widely studied, where the distance metrics between 3-D shape descriptors are usually hand-crafted. In this paper, motivated by the fact that deep neural network has the good ability to model nonlinearity, we propose to learn an effective nonlinear distance metric between 3-D shape descriptors for retrieval. First, the locality-constrained linear coding method is employed to encode each vertex on the shape and the encoding coefficient histogram is formed as the global 3-D shape descriptor to represent the shape...
December 28, 2016: IEEE Transactions on Cybernetics
Songchuan Zhang, Youshen Xia
Much research has been devoted to complex-variable optimization problems due to their engineering applications. However, the complex-valued optimization method for solving complex-variable optimization problems is still an active research area. This paper proposes two efficient complex-valued optimization methods for solving constrained nonlinear optimization problems of real functions in complex variables, respectively. One solves the complex-valued nonlinear programming problem with linear equality constraints...
December 28, 2016: IEEE Transactions on Cybernetics
Kai-Lung Hua, Hong-Cyuan Wang, Chih-Hsiang Yeh, Wen-Huang Cheng, Yu-Chi Lai
It is important to extract a clear background for computer vision and augmented reality. Generally, background extraction assumes the existence of a clean background shot through the input sequence, but realistically, situations may violate this assumption such as highway traffic videos. Therefore, our probabilistic model-based method formulates fusion of candidate background patches of the input sequence as a random walk problem and seeks a globally optimal solution based on their temporal and spatial relationship...
December 23, 2016: IEEE Transactions on Cybernetics
Jing Xie, Yi Mei, Andreas T Ernst, Xiaodong Li, Andy Song
In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously...
December 23, 2016: IEEE Transactions on Cybernetics
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