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IEEE International Symposium on Industrial Electronics (ISIE 2000)

作者： Garrido, R González, E Carvallo, A Gortcheva, E IPN CINVESTAV Dept Automat Control Mexico City 07000 DF Mexico

ISBN: (纸本)0780366069

In order to perform visual servoing tasks in a robotic sistem, one is confronted with the low sampling rate of standard cameras and the time delay introduced by image acquisition and processing. One way to circunvent the above problems is to estimate future positions of a moving object employing prediction techniques. In this work, three prediction techniques, namelly Kalman Filtering and two adaptive techniques employing least squares with forgetting factor and the projection algorithm respectively, are evaluated in terms of their prediction error and speed of convergence. Experimental results show that the adaptive technique employing the projection algorithm gives best results.

关键词： visual servoing Kalman filter least squares algorithm with forgetting factor projection algorithm features extraction

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Adaptive tracking control of the leader-following multi-agent system with uncertainties 35

Adaptive tracking control of the leader-following multi-agen...

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35th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Xinghong Zheng, Xiuli Henan Inst Technol Sch Elect Engn & Automat Xinxiang Henan Peoples R China

ISBN: (纸本)9798350334722

The adaptive tracking control problem of the multi-agent system is considered. The dynamics of each agent is of unknown parameter and can only receive different intensities of information from its different neighbors. To identify parameters, the improved projection algorithm is adopted, and based on each parameter estimate update law, the decentralized adaptive controllers are designed. Finally, a numerical example is provided to illustrate that each parameter estimate error is bounded and each agent tracks the desired reference trajectory.

关键词： adaptive tracking multi-agent system uncertainties projection algorithm

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Decision Tree Visualization for High-dimensional Numerical Data 5

Decision Tree Visualization for High-dimensional Numerical D...

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5th International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Szuecs, Dora Schmidt, Florian TU Berlin Complex & Distributed IT Syst Berlin Germany

ISBN: (纸本)9781538695883

Big Data enforces the usage of data mining techniques to provide the user valuable insights. There is a broad range of data mining and machine learning techniques tackling different tasks. Generic approaches are classification algorithms, which label given data points by a pretrained model. Decision tree-based classification algorithms are often used, as they provide a human-explainable model, which can be represented by simple induced rules. In order to present the classification results and the concrete model to the user, there exist for both problems a set of different solutions. Current visualizations either project labeled data into the plane or three-dimensional space, or the visualizations illustrate the decision tree rules as e.g. graph structures. But they lack to provide a possibility to show both, data and the model, within a single plot. Therefore, we propose a projection strategy to present both decision tree model and data in a single plot. Furthermore, we developed an interactive visualization to showcase the proposed approach and evaluated the visualization on open-source datasets. The results show that the plots can be computed in short time and projection adjustments are reasonably low.

关键词： Big data Decision trees Machine Learning Visualization High-dimensional data projection algorithm

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Complete Subhedge projection for Stepwise Hedge Automata

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algorithmS 2024年第8期17卷 339页

作者： Al Serhali, Antonio Niehren, Joachim Univ Lille Inria Ctr Lille France

We demonstrate how to evaluate stepwise hedge automata (Shas) with subhedge projection while completely projecting irrelevant subhedges. Since this requires passing finite state information top-down, we introduce the notion of downward stepwise hedge automata. We use them to define in-memory and streaming evaluators with complete subhedge projection for Shas. We then tune the evaluators so that they can decide on membership at the earliest time point. We apply our algorithms to the problem of answering regular XPath queries on Xml streams. Our experiments show that complete subhedge projection of Shas can indeed speed up earliest query answering on Xml streams so that it becomes competitive with the best existing streaming tools for XPath queries.

关键词： automata projection algorithm streaming algorithm Xml

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Design of Distributed Adaptive Control for a Class of Discrete -time Multi-agent Systems with Nonlinearity and Uncertainty 30

Design of Distributed Adaptive Control for a Class of Discre...

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30th Chinese Control and Decision Conference (CCDC)

作者： Li, Nannan Ma, Hongbin Zhang, Xinghong Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9781538612446

In this paper, a distributed adaptive control method is considered for a class of discrete-time multi-agent systems with nonlinearity and uncertainty. Each agent is affected by its neighbors, and there is a hidden agent as the leader in multi-agent systems who knows the desired reference signal. However, other agents are aware of neither the reference signal nor the existence of the hidden leader and leadership. In order to deal with uncertainty, a criteria function for each agent, which is consist of a weighted square combination of state errors and parameter errors with timevarying weighting factor, is adopted. By minimizing the criteria function, we propose a projection algorithm for each agent to estimate unknown parameters. Furthermore, we design a distributed adaptive controller for each agent using the information of its neighbors. Under the distributed adaptive control, the rigorous mathematical proof is presented to demonstrate that all the agents ultimately track the desired reference signal. FinAy, simulation results are given to illustrate the theoretical results.

关键词： Distributed Adaptive Control projection algorithm Nonlinear and Uncertain dynamics Discrete Time

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An improved parallel algorithm for sequence mining

An improved parallel algorithm for sequence mining

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IEEE International Conference on Mechatronics Automation

作者： She, Chundong Tang, Jian Li, Lei Wang, Hongbing Fan, Zhihua Chinese Acad Sci Inst Software Beijing 100080 Peoples R China

ISBN: (纸本)078039044X

It is more and more important in data mining field to finding the frequent sequences in a large database. The paper briefly introduces the basic concept of frequent sequence mining and presents the data parallel formulation and task parallel formulation of tree-projection based algorithm. Moreover, the on-line LPT algorithm is used to successfully solve the problem of imbalance for the task parallel formulation Our experiment shows that these algorithms are capable of achieving good speedups. However, the task parallel formulation is more scalable than the data parallel one.

关键词： parallel processing task decomposition projection algorithm frequent sequential patterns

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Parameter perturbation-free robust fuzzy high-gain observers

Parameter perturbation-free robust fuzzy high-gain observers

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Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010

作者： Hyun, Chang-Ho Ji, Seunghwan Div. of Electrical Electronic and Control Engineering Kongju National University Korea Republic of Test and Measurement Division R and D Center Innowireless Korea Republic of

This paper presents the design technique for parameter perturbation-free robust fuzzy high-gain observer. The authors have proposed the robust fuzzy high-gain observer [1]. In the previous work, nevertheless, only robust stability of the robust fuzzy high-gain observer based controller was considered. Therefore, the parameter perturbation problem was still existed. In order to overcome the problem, H ∞control technique and the adaptive projection algorithm based perturbation-free robust fuzzy high-gain observer is proposed in this paper. The proposed observer accomplishes the fast parameter convergence as well. Through the comparison with the research work of [1], the performance of the proposed method is verified in simulation.

关键词： Fast convergence H ∞control technique Perturbation-free projection algorithm

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Classical Discrete-Time Adaptive Control Revisited: Exponential Stabilization 1

Classical Discrete-Time Adaptive Control Revisited: Exponent...

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1st Annual IEEE Conference on Control Technology and Applications

作者： Miller, Daniel E. Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781509021826

Classical discrete-time adaptive controllers provide asymptotic stabilization. While the original adaptive controllers did not handle noise or unmodelled dynamics well, redesigned versions were proven to have some tolerance;however, exponential stabilization and a bounded gain on the noise was rarely proven. Here we consider a classical pole placement adaptive controller using the original projection algorithm rather than the commonly modifed version;we impose the assumption that the plant parameters lie in a convex, compact set and that the parameter estimates are projected onto that set at every step. We demonstrate that the closed-loop system exhibits very desireable closed-loop behaviour: there are linear-like convolution bounds on the closed loop behaviour, which confers exponential stability and a bounded noise gain. We emphasize that there is no persistent excitation requirement of any sort.

关键词： Adaptive control projection algorithm Exponential stability Bounded gain

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Iterative learning identification and control of discrete time-varying systems

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26th Chinese Control Conference

作者： Sun Mingxuan He Xiongxiong Zhejiang Univ Technol Coll Informat Engn Hangzhou 310032 Zhejiang Peoples R China

ISBN: (纸本)9787811240559

This paper presents discrete iterative learning control for systems with time-varying parametric uncertainties. Two prototype iterative learning identification algorithms, iterative learning projection and iterative learning least squares, are presented for estimating the time-varying unknowns. The main properties of the learning algorithms are explored for establishing the stability and convergence of the control scheme. The proof is based upon the use of a key technical lemma, which extends the existing one and tailored for the purpose of analysis in the iteration domain. It is shown that the complete tracking is achieved for every instant except for the initial instant, while the input and output signals of the controlled system remain bounded. The proposed scheme in this paper is applicable to tracking iteration-varying trajectories without any restriction on initial repositioning.

关键词： projection algorithm least squares iterative learning control time-varying parametrization

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Non-separable multidimensional multiresolution wavelets: A Douglas-Rachford approach

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APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2024年 73卷

作者： Franklin, David Hogan, Jeffrey A. Tam, Matthew K. Univ Newcastle Sch Math & Phys Sci Univ Dr Callaghan NSW 2308 Australia Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

After re-casting the wavelet construction problem as a feasibility problem with constraints arising from the requirements of compact support, smoothness and orthogonality, the Douglas-Rachford algorithm is employed in the search for multi-dimensional, non-separable, compactly supported, smooth, orthogonal, multiresolution wavelets in the case of translations along the integer lattice and isotropic dyadic dilations. An algorithm for the numerical construction of such wavelets is described. By applying the algorithm, new one-dimensional wavelets are produced as well as genuinely non-separable two-dimensional wavelets.

关键词： Wavelets Multiresolution analysis Optimisation Douglas-Rachford algorithm projection algorithm Feasibility problem

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