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5th International Conference on Intelligent Robotics and Applications, ICIRA 2012

作者： Chen, Yixiong Hu, Jin Zhang, Feng Hou, Zengguang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642335143

This paper proposes a functional electrical stimulation (FES)-involved control strategy for self-made exoskeleton lower limb rehabilitation robot for the training purpose of paraplegic patients caused by spinal cord injury (SCI) or stroke. Two muscles (Vastus Medialis and Riceps Femoris) are stimulated to produce active torque around knee joint which can be considered as a redundant actuator besides electrical motor. During the predefined trajectory tracking task, electrical motors compensate for the gravitational torque of the entire human-robot system, while the muscles provide torque calculated by a PD position/ velocity controller based on the tracking error. The FES-induced torque control is accomplished with combination of feedforward and feedback controller, former of which is obtained by applying off-line trained neural networks to map the relationship between desired active torque and FES parameters. Simulation results obtained by using Simulink toolboxes in Matlab verify the feasibility of this control strategy. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Torque control

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Adaptive optimal control for a class of nonlinear partially uncertain dynamic systems via policy iteration

Adaptive optimal control for a class of nonlinear partially ...

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2012 3rd International Conference on Intelligent control and Information Processing, ICICIP 2012

作者： Liu, Derong Yang, Xiong Li, Hongliang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781457721427

In this paper, by employing an online algorithm based on policy iteration (PI), an adaptive optimal control problem for continuous-time (CT) nonlinear partially uncertain dynamic systems is investigated. In this proposed algorithm, a discounted cost function is discussed, which is considered to be a more general case for optimal control problems. Two neural networks (NNs) are used to implement the algorithm, which aims at approximating the cost function and the control law, respectively. The uniform convergence to the optimal control is proven, and the stability of the system is guaranteed. An illustrating example is given. © 2012 IEEE.

关键词： Cost functions

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Generalized Hamilton-Jacobi-Isaacs formulation-based neural network H ∞ control for constrained input nonlinear systems

Generalized Hamilton-Jacobi-Isaacs formulation-based neural ...

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19th International Conference on Neural Information Processing, ICONIP 2012

作者： Huang, Yuzhu Liu, Derong Wei, Qinglai State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642344749

In this paper, the nearly H∞ optimal control solution for discrete-time (DT) constrained input nonlinear systems is considered. First, to deal with the input constraints, a quasi-norm performance index function is introduced. Second, the corresponding constrained Hamilton-Jacobi-Isaacs (HJI) equation is formulated, and then the quasi L 2-gain analysis of the nonlinear system is employed. Third, an iterative algorithm based on the game theoretic interpretation of the generalized HJI (GHJI) equation by using neural networks (NNs) is presented. Moreover, the convergence of the algorithm to the optimal saddle point solution is proved, and the stability is also proved. Finally, a simulation example is given to illustrate the performance of the proposed method. © 2012 Springer-Verlag.

关键词： Nonlinear systems

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Adaptive neural network control of an aerial work platform's arm

Adaptive neural network control of an aerial work platform's...

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10th World Congress on Intelligent control and Automation, WCICA 2012

作者： Jia, Pengxiao Li, En Zizhe Liang Yanhui Qiang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academic of Sciences Beijing 100190 China

ISBN: (纸本)9781467313988

An aerial work platform (AWP) is a type of off highway vehicle with a long beam to provide temporary access to inaccessible areas [1]. The motivation of the research is to increase its productivity, safety and reduce the manipulation complexity during the operation process. In this paper, a simplified two-link model of AWP's arm is given. The control scheme based on neural network modeling technology is employed to steer the AWP's arm to track the desired trajectories asymptotically, which requires neither the evaluation of inverse dynamical model nor the time-consuming training process. The simulation results validate the effectiveness of the proposed approach. © 2012 IEEE.

关键词： Antennas

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How shall we catch people's concerns in micro-blogging?

How shall we catch people's concerns in micro-blogging?

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21st Annual Conference on World Wide Web, WWW'12

作者： Gao, Heng Li, Qiudan Bao, Hongyun Song, Shuangyong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781450312301

In micro-blogging, people talk about their daily life and change minds freely, thus by mining people's interest in micro-blogging, we will easily perceive the pulse of society. In this paper, we catch what people are caring about in their daily life by discovering meaningful communities based on probabilistic factor model (PFM). The proposed solution identifies people's interest from their friendship and content information. Therefore, it reveals the behaviors of people in micro-blogging naturally. Experimental results verify the effectiveness of the proposed model and show people's social life vividly. Copyright is held by the author/owner(s).

关键词： Blogs

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Social manufacturing cloud service platform for the mass customization in apparel industry

Social manufacturing cloud service platform for the mass cus...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Xiuqin Shang Xiwei Liu Gang Xiong Changjian Cheng Yonghong Ma Timo R. Nyberg The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Dongguan China Dongguan KAIN Electronic Science & Technology Company Limited Dongguan China BIT Research Centre Aalto University Finland

ISBN: (纸本)9781479905287

Social Manufacturing is a novel manufacturing mode, which can introduced in the apparel industrial and other fashion industries for the mass customization, based on network, 3D fitting mirror and other technologies. In social manufacturing, the consumers are involved fully into the production process by the internet; the manufacturing equipments and the intelligent interactive service terminals (3D fitting mirrors) are online, forming the manufacture and service equipment network; to realize the social manufacturing, a powerful modern logistics system are needed to support the production and E-business. Therefore, the manufacture and service equipment network, mass-customization or mass-rent webs involving a large number of people, and modern logistics network are combined in the social manufacturing platform by using the network, cloud technology, and other technologies. in the near future everyone will need the novel manufacture mode and the traditional enterprises, such as apparel enterprises, will be transformed into the intelligent enterprises which can proactively perceive and respond the personalized demands of the large quantity of consumers, and realize the mass customization by the social manufacture cloud service platform.

关键词： Three-dimensional displays Fitting Mirrors Clothing Production Business

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A weighted pattern recognition algorithm for short-term traffic flow forecasting

A weighted pattern recognition algorithm for short-term traf...

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2012 9th IEEE International Conference on Networking, Sensing and control, ICNSC 2012

作者： Li, Shuangshuang Shen, Zhen Wang, Fei-Yue State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Beijing 100190 China

ISBN: (纸本)9781467303880

The k-nearest neighbor (k-NN) nonparametric regression is a classic model for single point short-term traffic flow forecasting. The traffic flows of the same clock time of the days are viewed as neighbors to each other, and the neighbors with the most similar values are regarded as nearest neighbors and are used for the prediction. In this method, only the information of the neighbors is considered. However, it is observed that the "trends" in the traffic flows are useful for the prediction. Taking a sequence of consecutive time periods and viewing the a sequence of "increasing", "equal" or "decreasing" of the traffic flows of two consecutive periods as a pattern, it is observed that the patterns can be used for prediction, despite the patterns are not from the same clock time period of the days. Based on this observation, a pattern recognition algorithm is proposed. Moreover, empirically, we find that the patterns from different clock time of the days can have different contributions to the prediction. For example, if both to predict the traffic flow in the morning, the pattern from the morning can lead to better prediction than same patterns from afternoon or evening. In one sentence, we argue that both the pattern and the clock time of the pattern contain useful information for the prediction and we propose the weighted pattern recognition algorithm (WPRA). We give different weights to the same patterns of different clock time for the prediction. In this way, we take both virtues of the k-NN method and the PRA method. We use the root mean square error (RMSE) between the actual traffic flows and the predicted traffic flows as the measurement. By applying the results to actual data and the simulated data, about 20% improvement compare with the PRA is obtained. © 2012 IEEE.

关键词： Forecasting

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Click frauds and price determination models

Click frauds and price determination models

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2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012

作者： Li, Xiarong Zeng, Daniel Wang, Lei State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Arizona United States

ISBN: (纸本)9781467321037

Click fraud (CF) has become a serious problem in the online advertising, making the anti-CF issue quite important. In this paper, we analyze the effects of the price determination model on the CF situations in online advertising. Our theoretical results show that the flat-rate model can induce more click frauds than the real-time-bidding model. Our finding is validated with a real advertising dataset. © 2012 IEEE.

关键词： Marketing

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Listwise approaches based on feature ranking discovery

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Frontiers of Computer Science 2012年第6期6卷 647-659页

作者： Yongqing WANG Wenji MAO Daniel ZENG Fen XIA Commercial Products Development Department Alibaba Inc.Hangzhou 310052China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Management Information Systems University of ArizonaTucsonAZ 85721USA Union Research and Development Department Baidu Inc.Beijing 100085China

Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BL-FeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.

关键词： learning to rank listwise approach feature＇s ranking discovery

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Reaching an Average Consensus in Noisy Environments

Reaching an Average Consensus in Noisy Environments

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第八届中国多智能体系统与控制会议(MASC'2012)

作者： WANG Yunpeng CHENG Long HOU Zeng-Guang TAN Min Sate Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

This paper studies a mean square average consensus of general linear discrete-time time-invariant multi-agent systems with communication noises.A distributed protocol,which is composed of the agent’s own state feedback and the relative states between the agent and its neighbors,is proposed.A time-varying consensus gain is applied to attenuate the effect of noises.A polynomial,namely “parameter polynomial”,is constructed in such a way that its coefficients are the paraments in the control gain vector of the proposed *** turns out that the parameter polynomial plays an important role in the consensus analysis of linear multi-agent *** is proved that under the proposed protocol the necessary and sufficient conditions for ensuring the mean square average consensus are: the communication topology graph is balanced and strongly connected;the consensus gain satisfies the approximation-type conditions;and all roots of the parameter polynomial are in the unit circle.

关键词： Multi-agent systems discrete-time average consensus mean square noise linear time-invariant

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