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A New Self-Learning Optimal control Scheme for Discrete-Time Nonlinear systems Using Policy Iterative Adaptive Dynamic Programming

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IFAC Proceedings Volumes 2013年第20期46卷 580-585页

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

In this paper, a new self-learning method using policy iterative adaptive dynamic programming (ADP) is developed to obtain the optimal control scheme of discrete-time nonlinear systems. The iterative ADP algorithm permits an arbitrary admissible control law to initialize the iterative algorithm. It is the first time that the properties of the policy iterative ADP are established for the discrete-time situation. It proves that the iterative performance index function is non-increasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. It also proves that any of the iterative control policy can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, a simulation example is given to illustrate the performance of the present method.

关键词： Adaptive Dynamic Programming Approximate Dynamic Programming Nonlinear System Optimal control Policy Iteration Neural Networks Reinforcement Learning

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Haptic interfaces for the rescue walking robots motion in the disaster areas

Haptic interfaces for the rescue walking robots motion in th...

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UKACC International Conference on control (control)

作者： Luige Vladareanu Octavian Melinte Adrian Bruja Hongbo Wang Xiaojie Wang Shuang Cang Hongnian Yu Zeng-Guang Hou Xiao-Liang Xie Robotics and Mechatronics Department Institute of Solid Mechanics C-tin Mille 15 Bucharest 1 ROMANIA School of Design Engineering and Computing Boumemouth University Poole Dorset UK Parallel Robot and Mechatronic System Laboratory of Hebei Province University Qinhuangdao CHINA State Key Laboratory of Management and Control for Complex Systems Beijing P.R.CHINA

In the recent years haptic interfaces became a reliable solution in order to solve problems which arise when humans interact with the environment. If in the research area of the haptic interaction between human and environment there are important researches, a innovative approach for the interaction between the robot and the environment using haptic interfaces and virtual projection method is presented in this paper. In order to control this interaction we used the Virtual Projection Method where haptic control interfaces of impedance and admittance will be embedded. The obtained results, validated by simulations assure stability, stiffness, high maneuverability and adaptability for rescue walking robots in order to move in disaster, dangerous and hazardous areas.

关键词： Haptic interfaces Legged locomotion Robot sensing systems Impedance Force

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Neuro-Optimal control of Unknown Nonaffine Nonlinear systems with Saturating Actuators

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IFAC Proceedings Volumes 2013年第20期46卷 574-579页

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

This paper develops an adaptive optimal control for the infinite-horizon cost of unknown nonaffine nonlinear continuous-time systems with control constraints. A recurrent neural network (NN) is constructed to identify the unknown system dynamics with stability proof. Then, two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal value, respectively. By using this architecture, the action NN and the critic NN are tuned simultaneously, without the requirement of the knowledge of system dynamics. In addition, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded based on Lyapunov's direct method. A simulation example is provided to verify the effectiveness of the developed theoretical results.

关键词： Actuator saturation Adaptive control Neural networks Nonaffine systems Optimal control Unknown nonlinear systems

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EMG-based Estimation of Knee Joint Angle under Functional Electrical Stimulation Using an Artificial Neural Network

EMG-based Estimation of Knee Joint Angle under Functional El...

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第三十二届中国控制会议

作者： Yixiong Chen Jin Hu Feng Zhang Pengfeng Li Zengguang Hou State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781479900305

In this paper,an artificial neural network is proposed to estimate knee joint angle in hybrid activation of knee extension motion,including voluntary muscle contraction and functional electrical stimulation(FES) induced *** electromyography(EMG) signals of three muscles responsible for knee extension and FES parameter which describe the FES intensity are used as input vector of the neural network,while the estimated knee angle is the *** the experiment, FES with different combinations of parameters(pulse amplitude and pulse width) was delivered to the rectus femoris muscle of a healthy male subject when the knee was in a periodic extension motion by voluntary muscle *** EMG signals of three muscles,parameters of FES as well as the actual knee angle were ***,there were 52,233 and 17,420 sampling points corresponding to 261 and 87 seconds used to train and validate the neural *** result shows the trained network has a satisfactory performance on knee joint angle estimation whose output well follows the curve of actual knee angle. Root mean square error between estimated angle and actual angle is employed to represent the estimation accuracy which is 5.07 degree according to the experimental data.

关键词： Functional electrical stimulation Electromyography Neural network

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Action Knowledge Extraction from Web Text

Action Knowledge Extraction from Web Text

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IEEE International Conference on Intelligence and Security Informatics

作者： Ansheng Ge Wenji Mao Daniel Zeng Lei Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467362146

Action knowledge is an important type of behavioral knowledge and of vital importance to many applications in social computing, especially in behavior modeling, analysis and prediction. In this paper, we present a computational method to action knowledge extraction from online media. Our approach is based on mutual bootstrapping and combined with knowledge reasoning. Compared with the related work, our approach can acquire more types of action knowledge, and needs much less human labor. We evaluate the performance of our method using the Web textual data from security informatics domain. The experimental results show the effectiveness of our proposed method.

关键词： Action knowledge Knowledge extraction Bootstrapping Reasoning

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Predicting User Participation in Social Networking Sites

Predicting User Participation in Social Networking Sites

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IEEE International Conference on Intelligence and Security Informatics

作者： Qingchao Kong Wenji Mao Daniel Zeng State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467362146

Social networking sites provide a convenient way for users to participate in discussion groups and communicate with others. While users situate in and enjoy such a social environment, it is important for various security related applications to understand, model and analyze participating users' behavior. In this paper, we make an attempt to model and predict user participation behavior in discussion groups of social networking sites. Our work employs a feature-based approach, which considers four types of features: thread features, content similarity, user behavior and social features. We conduct an empirical study on a popular social networking site in China, ***. The experimental results show the effectiveness of our approach.

关键词： User participation Behavior modeling and prediction Social networking sites

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Label micro-blog topics using the Bayesian inference method

Label micro-blog topics using the Bayesian inference method

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Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2013

作者： Gao, Heng Li, Qiudan Zheng, Xiaolong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan 523808 China

ISBN: (纸本)9783642396922

Classifying Micro-blog content is a popular research topic in social media, which can help users access their favorite information quickly. Much research focuses on classifying Micro-blog content with short text dataset. The challenge is the classification effect may be hampered by content ambiguity. To address this challenge, we propose a novel classification framework using external knowledge base and the Bayesian inference method. We first introduce Baidu Encyclopedia to extract effective features, and then we train efficient classifiers with the probabilistic graphic model. The proposed model can classify micro-blog content reliably. Experiments on Sina-weibo dataset demonstrate the effectiveness of the proposed method. © 2013 Springer-Verlag.

关键词： Classification (of information)

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Direct adaptive output feedback control for MIMO uncertain nonlinear discrete-time systems based on neural networks

Direct adaptive output feedback control for MIMO uncertain n...

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Chinese control Conference (CCC)

作者： Xiong Yang Derong Liu Ding Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

This paper develops a novel neural-network-based direct adaptive control scheme for a class of multi-input-multi-output uncertain nonlinear discrete-time (DT) systems in the presence of unknown bounded disturbances. By employing feedback linearization methods, neural network (NN) approximation can cancel the nonlinearity of the DT systems. Meanwhile, the weights of NNs are directly updated online instead of preliminary offline training. In addition, unlike most literatures, the condition for persistent excitation is removed. Based on Lyapunov's direct method, both tracking errors and weight estimates are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. Finally, an example is provided to demonstrate the effectiveness of the proposed approach.

关键词： Artificial neural networks Nonlinear systems Vectors Adaptive control Trajectory MIMO

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What is the uniqueness of growth pattern in human flesh search organization?

What is the uniqueness of growth pattern in human flesh sear...

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Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2013

作者： Wang, Tao Zhang, Qingpeng Fu, Julei Wang, Xiao Zheng, Suncong College of Information System and Management National University of Defense Technology Changsha 410073 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Systems and Industrial Engineering University of Arizona Tucson AZ United States

ISBN: (纸本)9783642396922

Human Flesh Search is an explosive Web phenomenon these years in China, especially when new media, such as Weibo, appeared. In this research, we present the empirical studies about growing patterns of participated Human Flesh Search user, and unveiled one unique growing property. Based on the empirical findings, we compare it with two others results, one is the common Weibo's growth pattern, and the other is the popularity growth pattern of photo in Flickr. Some differences are proposed and analyzed briefly. © 2013 Springer-Verlag.

关键词： Social networking (online)

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A Prior-Free Encode-Decode Change Detection Test to Inspect Datastreams for Concept Drift

A Prior-Free Encode-Decode Change Detection Test to Inspect ...

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International Joint Conference on Neural Networks

作者： Cesare Alippi Li Bu Dongbin Zhao Politecnico di Milano Italy and The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467361279

Online change detection in datastreams has attracted many researchers and is becoming a very hot topic whose relevance will further increase with research on Big Data. Concept drift is induced by changes in stationarity of the process generating the data caused by faults, time variance of the environment and inaccuracy of the change detection mechanism. Here, we propose a recurrent auto-associative Encode-Decode machine trained to reconstruct input data. The generated residual is then inspected for structural changes with a Change Detection Test (CDT). Although any CDT can be used, in the paper we focus the attention on the Hierarchical Intersection of Confidence Intervals change detection test for its capability of controlling false positives with a two layered test and an online version of the Lepage Change Point Model. Once concept drift is detected, the designed Encode-Decode machine, globally acting as an Encode-Decode CDT, is retrained on new data to detect subsequent changes.

关键词： Concept Drift CDT detect subsequent changes

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