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Neural and Fuzzy Dynamic Programming for Under-actuated Systems

Neural and Fuzzy Dynamic Programming for Under-actuated Syst...

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International Joint Conference on Neural Networks

作者： Dongbin Zhao Yuanheng Zhu Haibo He State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Electrical Computer and Biomedical Engineering University of Rhode Island

ISBN: (纸本)9781467314886

This paper aims to integrate the fuzzy control with adaptive dynamic programming (ADP) scheme, to provide an optimized fuzzy control performance, together with faster convergence of ADP for the help of the fuzzy prior knowledge. ADP usually consists of two neural networks, one is the Actor as the controller, the other is the Critic as the performance evaluator. A fuzzy controller applied in many fields can be used instead as the Actor to speed up the learning convergence, because of its simplicity and prior information on fuzzy membership and rules. The parameters of the fuzzy rules are learned by ADP scheme to approach optimal control performance. The feature of fuzzy controller makes the system steady and robust to system states and uncertainties. Simulations on under-actuated systems, a cart-pole plant and a pendubot plant, are implemented. It is verified that the proposed scheme is capable of balancing under-actuated systems and has a wider control zone.

关键词： Adaptive dynamic programming Fuzzy control Neural network Underactutaed system Cart-pole plant Pendubot

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Modeling and analysis of information dissemination mechanism of social media

Modeling and analysis of information dissemination mechanism...

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

作者： Jiachen Hou Gang Xiong Dong Fan Timo R. Nyberg State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Dongguan China Institute of Automation Chinese Academy of Sciences Beijing Beijing CN Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Dongguan China BIT Research Centre Aalto University Aalto Finland

With the fast development of Internet and data volume increase in Internet, Internet is playing more important role in information spreading in recent years. It is reported that social network is a typical complex network expressed with scale free degree. Emergency cases are happening more and more often in China and all over the world, the governments has realized that it is necessary to monitor and manage the information spreading on social network. In this paper, a new experiment platform frame, which is based on SPA (Spreading Activation) model and social centrality, is proposed to validate the information spreading speed in social network, to help the administration department to manage social network more efficiently.

关键词： Computational modeling Media Data models World Wide Web

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Human Behavior Imitation for a Robot to Play Table Tennis

Human Behavior Imitation for a Robot to Play Table Tennis

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The 24th Chinese control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

作者： Qizhi Wang Xianqiang Zhang De Xu School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 PR Chin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese

The main purpose of this paper is to make table tennis robots complete the hit table tennis ball action by imitating human’s behavior. The main strategy is to record a video of action which people played the table tennis, then analysis the video of the racket trajectory. The racket in the image is extracted by image processing when the each frame is captured in the video. Then three-dimensional coordinates of the center of the racket and racket posture are obtained via PnP positioning approach based on the intrinsic parameters of the camera. The table tennis robot will off-line learn to complete the imitation of basic actions of the racket trajectory and postures. A large number of experimental data is used to establish basic actions of table tennis robot.

关键词： Imitation off-line learning the trajectory of the racket table tennis robot PnP positioning method

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When multivariate forecasting meets unsupervised feature learning - Towards a novel anomaly detection framework for decision support

When multivariate forecasting meets unsupervised feature lea...

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International Conference on Information Systems, ICIS 2012

作者： Wang, Jiawei Ren, Jimmy Sj Wang, Wei Li, Xin Li, Qiudan Liao, Stephen S.Y. Department of Information Systems USTC-CityU Joint Advanced Research Centre 166 Ren'ai Road Suzhou China Department of Information Systems City University of Hong Kong 83 Tat Chee Avenue Hong Kong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781627486040

Many organizations adopt information technologies to make intelligent decisions during operations. Time-series data plays a crucial role in supporting such decision making processes. Though current studies on time-series based decision making provide reasonably well results, the anomaly detection essence underling most of the scenarios and the plenitude of unlabeled data are largely overlooked and left unexplored. We argue that by using multivariate forecasting and unsupervised feature learning, these two important research gaps could be filled. We carried out two experiments in this study to testify our approach and the results showed that decision support performance was significantly improved. We also proposed a novel framework to integrate the two methods so that our approach may be generalized to a larger problem domain. We discussed the advantages, the limitations and the future work of our study. Both practical and theoretical contributions were also discussed in the paper. © 2012 by the AIS/ICIS Administrative Office All rights reserved.

关键词： Decision support systems

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Emergency traffic evacuation control based on the orthogonal experimental design method

Emergency traffic evacuation control based on the orthogonal...

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International Conference on intelligent Transportation

作者： Yisheng Lv Fenghua Zhu Qinghai Miao Peijun Ye Songhang Chen State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing China

Evacuees' behaviors have a significant impact on evacuation efficiency. Evacuation clearance time is one of the key indicators in the evacuation planning and management. Evacuees' departure time choice, destination choice and route choice behavior based on real-time traffic information are three crucial factors used to estimate evacuation clearance time. In this paper, we use the orthogonal experimental design method to determine values of these three factors simultaneously, which can reduce evacuation clearance time significantly. Once we obtain these values, we can guide evacuees' departure time choice, destination choice and route choice behavior to approximate these values and evacuation efficiency will improve greatly. The dynamic traffic evacuation process is modeled using a rule-based multi-agent microscopic traffic simulation system called TransWorld. The simulation experiment results illustrate the effectiveness and reliability of the proposed method. The proposed methodology, computational results and discussions can be used for future emergency evacuation planning and management.

关键词： Transportation Computational modeling Real-time systems Design methodology Object oriented modeling Planning Traffic control

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ACP based 3D Emergency Drills System for Petrochemical Plants

ACP based 3D Emergency Drills System for Petrochemical Plant...

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World Congress on intelligent control and automation

作者： Sifeng Jing Changjian Cheng Gang Xiong Xiwei Liu Xiuqin Shang Weixing Wang State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Dongguan Research Institute of CASIA Cloud Computing Industrial Technology Innovation and Incubation Center Chinese Academy of Sciences

ISBN: (纸本)9781467313971

To further enhance emergency management skills of an organisation's emergency response personnel, emergency response training, especially 3D emergency drill, is currently becoming more and more important in the petrochemical sector. So, a novel 3D emergency drills system is designed and developed based on ACP approach, which can be used for mocking emergency response plan drills and evaluating the plan. A case study reveals that the performance of the system is good and the system can meet the needs of emergency response training and optimizing emergency response plan in petrochemical plants.

关键词： ACP Approach Artificial Systems Computational Experiments Parallel Execution Mock-Drill Emergency Response Training

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A hierarchical neural network architecture for classification

A hierarchical neural network architecture for classificatio...

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9th International Symposium on Neural Networks, ISNN 2012

作者： Wang, Jing He, Haibo Cao, Yuan Xu, Jin Zhao, Dongbin Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI 02881 United States MathWorks Inc. Natick MA 01760 United States Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ 07030 United States State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642313455

In this paper, a hierarchical neural network with cascading architecture is proposed and its application to classification is analyzed. This cascading architecture consists of multiple levels of neural network structure, in which the outputs of the hidden neurons in the higher hierarchical level are treated as an equivalent input data to the input neurons at the lower hierarchical level. The final predictive result is obtained through a modified weighted majority vote scheme. In this way, it is hoped that new patterns could be learned from hidden layers at each level and thus the combination result could significantly improve the learning performance of the whole system. In simulation, a comparison experiment is carried out among our approach and two popular ensemble learning approaches, bagging and AdaBoost. Various simulation results based on synthetic data and real data demonstrate this approach can improve the classification performance. © 2012 Springer-Verlag.

关键词： Adaptive boosting

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Reinforcement Learning control Based on Multi-Goal Representation Using Hierarchical Heuristic Dynamic Programming

Reinforcement Learning Control Based on Multi-Goal Represent...

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International Joint Conference on Neural Networks

作者： Zhen Ni Haibo He Dongbin Zhao Danil V. Prokhorov Department of Electrical Computer and Biomedical Engineering University of Rhode Island State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Toyota Research Institute NA TTC

ISBN: (纸本)9781467314886

We are interested in developing a multi-goal generator to provide detailed goal representations that help to improve the performance of the adaptive critic design (ACD). In this paper we propose a hierarchical structure of goal generator networks to cascade external reinforcement into more informative internal goal representations in the ACD. This is in contrast with previous designs in which the external reward signal is assigned to the critic network directly. The ACD control system performance is evaluated on the ball-and-beam balancing benchmark under noise-free and various noisy conditions. Simulation results in the form of a comparative study demonstrate effectiveness of our approach.

关键词： ACD gene Automatic Shutoff Devices noise-free critic hierarchical structure Noise learning (artificial intelligence) dynamic programming Generator dynamos Heuristics

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Identifying Important Users in Sina Microblog

Identifying Important Users in Sina Microblog

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International Conference on Multimedia Information Networking and Security (MINES)

作者： Jiaqi Liu Zhidong Cao Kainan Cui Feng Xie State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Tsinghua National Lab for Information Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781467330930

Important users are high-status vertices in social networks. They are everywhere in most fields of society and have big impact on those around them. Although a lot of effort has been made on identifying important users, the efficient methods still need to be developed, especially for the web users from Sina microblog, which is the most popular social networking sites in China and has unique characteristics. In this paper, a machine learning-based method which only uses several attributes on Naive Bayes Classifiers (NBC) and Back Propagation Neural Network (BPNN) was proposed to identify important users. Initial experiments indicate that our method is effective. The result of "high" category has more than 55% accuracy rate. We find the NBC can identify more important users while BPNN has higher accuracy rate. What's more, the numbers of follower and followings in Sina microblog is independent.

关键词： Social network services Training Neural networks Accuracy Security Learning systems Educational institutions

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Research on SVM and FLDA in classification with comparative experiments

Research on SVM and FLDA in classification with comparative ...

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IEEE International Conference on Networking, Sensing and control

作者： Yegan Qian Gang Xiong Yanjie Yao Anhui Radio TV Station Hefei Hanteng Information Tech Company Hefei Anhui China State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

The paper discusses two important classification techniques, Fisher's linear discriminated analysis (FLDA) and Support Vector Machine (SVM). First, we propose a theoretical discussion, and then implement FLDA and SVM on several datasets of two classes and multiclass, a comparative experimental analysis among these two techniques aims at exploring and assessing the performance of FLDA and SVM classifiers. To sustain such analysis, the two classification techniques are compared with different training data sets and testing data sets. Different performance indicators have been used to support our experimental studies in a detailed and accurate way such as the classification accuracy. The results obtained on different datasets conclude that FLDA and SVM are valid and effective approaches for pattern classification and conclude their different performance and problems with different size datasets. Meanwhile, the paper employs a non-traditional method to get the training and testing data set, and concludes detailed pros and cons from the experiment results.

关键词： Support vector machines Training Testing Accuracy Vectors Head Magnetic heads

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