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PD-tree: A new approach to subtree discovery

WSEAS Transactions on Information Science and Applications 2005年第11期2卷 1772-1779页

作者： Ivancsy, Renata Vajk, Istvan Department of Automation and Applied Informatics HAS-BUTE Control Research Group Budapest University of Technology and Economics 1111 Goldmann Gy. ter 3. Budapest Hungary

Discovering frequent patterns in semi-structured and structured data is a new trend in the data mining community. The motivation for discovering such types of patterns can be understood when investigating the real world problems. Several problems can be modeled only with structural data like graphs rather than with flat transactions. Thus the research directions must adjust to the requirements of these new kinds of problems. Frequent tree mining is a special part of frequent graph mining as trees are special graphs. Trees can model several real world problems such as handling XML documents or WEB navigation patterns, furthermore, trees can be handled easier than general graphs. Thus discovering frequent trees has justification. In this paper a novel approach is proposed for efficiently discovering frequent tree-like patterns in tree databases. The new algorithm presented in this paper is a level-wise, "candidate generate and test" algorithm. The main contribution of the new approach is to use pushdown automatons for subtree inclusion testing. In order to enhance the performance of counting the support of the candidate trees, the several automatons are joined such that only one stack is needed for handling the joined automatons. The advantage of the new method is that is has to read the items of the input exactly once while the support of all the candidates is determined.

关键词： Data mining

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Efficient sequential pattern mining algorithms

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WSEAS Transactions on Computers 2005年第2期4卷 96-101页

作者： Ivancsy, Renata Vajk, Istvan Department of Automation and Applied Informatics HAS-BUTE Control Research Group Budapest University of Technology and Economics Goldmann Gy. ter 3. H-1111 Budapest Hungary

Sequential pattern mining is a heavily researched area in the field of data mining with wide variety of applications. The task of discovering frequent sequences is challenging, because the algorithm needs to process a combinatorially explosive number of possible sequences. Most of the methods dealing with the sequential pattern mining problem are based on the approach of the traditional task of itemset mining, because the former can be interpreted as the generalization of the latter. Several algorithms use a level-wise "candidate generate and test" approach, while others use projected databases to discover the frequent sequences. In this paper a classification of the well-known sequence mining algorithm is presented. Because each algorithm has its own advantages and drawbacks regarding the execution time and the memory requirements, and the exact aim of the algorithms differs as well, thus an exact ranking of the methods is omitted. A basic level-wise algorithm, the GSP is described in detail. Because the level-wise algorithms need less memory in general than the projection-based ones, ah efficient implementation of the GSP algorithm is also suggested. Two novel methods, the Bitmap-based GSP (BGSP) and the SM-Tree (State Machine-Tree) algorithms are presented as an enhancement of the GSP-based sequential pattern mining approach.

关键词： Data mining

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Subspace identification methods: review and re-interpretation

Subspace identification methods: review and re-interpretatio...

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International Conference on control and automation (ICCA)

作者： I. Vajk J. Hetthessy Department of Automation and Applied Informatics HAS-BUTE Control Research Group Budapest University슠of슠Technology슠and슠Economics Budapest Hungary

The paper is devoted for a systematic discussion on the subspace based identification methods. The structure of the noise components acting additively at various system signals is considered as the guiding principle for the analysis of the estimation algorithms. It is shown that replacing the state space approach typically applied in this field by a discrete time transfer function system realization the highly involved machinery of the subspace algorithms can be handled in a clearly ordered framework exhibited by a set of over-determined equations to be solved.

关键词： Observability Transfer functions Parameter estimation State-space methods Equations Automatic control controllability Signal analysis Algorithm design and analysis Machinery

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Robots control based on parameter identification and adaptive gain smooth sliding observer-controller

Robots control based on parameter identification and adaptiv...

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作者： Filipescu, A. Dugard, L. Stamatescu, S. Department of Automation Applied Informatics and Electronics University Dunarea de Jos of Galati Domneasca no.47 800008Galati Romania Laboratoire d'Automatique Grenoble CNRS/INPG UMR 5528 ENSIEG-BP 46 38402 Saint Martin d'Hères Cedex France ASTI Control S.A Calea Plevnei 139 Bucharest Romania

ISBN: (纸本)008045108X

an adaptive gain, smooth sliding observer-controller is developed to control n -degree of freedom rigid robotic manipulators with uncertain parameters. Furthermore, an on-line closed loop identification scheme, for time-varying parameters is proposed in order to obtain useful information despite loads, external disturbances and faults detection. in order to reduce the chattering, a smooth switching function (parameterised tangent hyperbolic function) is used instead of pure relay one, in the observer and the controller. The gains of the switching functions are adaptively updated, depending on the estimation and tracking errors, respectively. Using adaptive gains, the transient and tracking responses are improved. Simulation results with a two degree of freedom (DOF) robot manipulator are presented to show the interest of the approach. Copyright © 2005 IFAC.

关键词： Hyperbolic functions

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ROBOTS control BASED ON PARAMETER IDENTIFICATION AND ADAPTIVE GAIN SMOOTH SLIDING OBSERVER-controlLER

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IFAC Proceedings Volumes 2005年第1期38卷 511-516页

作者： A. FILIPESCU L. DUGARD S. STAMATESCU University “Dunarea de Jos” of Galati Department of Automation Applied Informatics and Electronics Domneasca no.47 800008 Galati Romania Laboratoire d'Automatique Grenoble CNRS/INPG UMR 5528 ENSIEG-BP 46 38402 Saint Martin d'Hères Cedex France ASTI Control S.A Calea Plevnei 139 Bucharest Romania Sabin@asticontrol.ro

An adaptive gain, smooth sliding observer-controller is developed to control n -degree of freedom rigid robotic manipulators with uncertain parameters. Furthermore, an on-line closed loop identification scheme, for time-varying parameters is proposed in order to obtain useful information despite loads, external disturbances and faults detection. In order to reduce the chattering, a smooth switching function (parameterised tangent hyperbolic function) is used instead of pure relay one, in the observer and the controller. The gains of the switching functions are adaptively updated, depending on the estimation and tracking errors, respectively. Using adaptive gains, the transient and tracking responses are improved. Simulation results with a two degree of freedom (DOF) robot manipulator are presented to show the interest of the approach.

关键词： Robotic manipulators adaptive gain smooth sliding observer-controller parameter identification

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Nonlinear process identification and predictive control by multi-parameter models 6

Nonlinear process identification and predictive control by m...

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6th IFAC Symposium on Nonlinear control Systems, NOLCOS 2004

作者： Schmttz, U. Haber, R. Bars, R. Department of Plant and Chemical Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KölnD-50679 Germany Department of Automation and Applied Informatics Budapest University of Technology and Economics Goldmann Gy. ter 3 BudapestH-1111 Hungary

According to the multi-model approach a nonlinear dynamical process is approximated in different working points by local valid linear models. The global valid model output is calculated as the weighted sum of the sub-model outputs. The parameters of the Gaussian weighting function can be chosen by optimization. The computation time can be reduced if instead of the model outputs the parameters (for example static gain and time constant) of the local valid models are merged. The global valid nonlinear model can be used e.g., for model based predictive control. The new, multi-parameter method is illustrated by a heat exchanger example. Copyright © 2004 IEAC.

关键词： Heat exchangers

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Hybrid IMC dead-beat controller design in delta domain 2

Hybrid IMC dead-beat controller design in delta domain

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2nd IFAC Conference on control Systems Design, CSD 2003

作者： Preit, Z. Bars, R. Vajk, I. Haber, R. Department of Automation and Applied Informatics Budapest University of Technology and Economics MTA-BME Control Research Group Goldmann Gyter 3 BudapestH-1111 Hungary Department of Plant and Process Engineering University of Applied Science Cologne Germany

The paper presents a control system design technique in delta domain for IMC (Internal Model control) structure. Also a generalised form for delta domain Dead-beat control algorithm is given, a hybrid implementation of the controller (Z-domain combined with delta domain) is presented and its architecture is compared with the pure deltadomain implementation. The effect of placing the limitations in the IMC structure is also studied. The hybrid control structure is compared with a similar Dead-beat controller in Z-domain for second and third order plants (benchmarks). An illustrative sensitivity analysis between the hybrid control system and the Z-domain system has been performed. © 2003 IFAC.

关键词： controllers

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Long-range optimal model and multi-step-ahead prediction identification for predictive control 13

Long-range optimal model and multi-step-ahead prediction ide...

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13th IFAC Symposium on System Identification, SYSID 2003

作者： Haber, R. Schmitz, U. Bars, R. Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KölnD-50679 Germany Department of Automation and Applied Informatics Control Research Group Hungarian Academy of Sciences Budapest University of Technology and Economics Hungary

Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements are noisy or/and model structure differs from real process structure. In this paper two different identification schemes are presented and compared: long-range predictive single-model identification and simultaneous multi-step-ahead prediction identification. It is shown that the first method is easier to realize but the second one leads to more accurate results. Both methods are derived for a first-order model in details. Simulation runs and a level control example illustrate the algorithms presented. © 2003 International Federation of Automatic control.

关键词： Parameter estimation

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Direct learning control design for a class of linear time-varying switched systems

Direct learning control design for a class of linear time-va...

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Fourth International Conference on control and automation

作者： Xu, Jian-Xin Korondi, Peter Yan, Rui Guan, Zhi-Hong Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Department of Automation and Applied Informatics Budapest University of Technology and Economics Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)078037777X

In this paper, a direct learning control method for a class of switched systems is proposed. The objective of direct learning is to generate the desired control profile for a newly switched system without any feedback, even if the system may have uncertainties. This is achieved by exploring the inherent relationship between any two systems before and after a switch. The new method is applicable to a class of linear time varying, uncertain and switched systems, when the trajectory tracking control problem is concerned. A numerical simulation demonstrates the effectiveness of the proposed method. © 2003 IEEE.

关键词： Time varying control systems

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Optimal choice of horizons for predictive control by using genetic algorithms 2

Optimal choice of horizons for predictive control by using g...

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2nd IFAC Conference on control Systems Design, CSD 2003

作者： Haber, R. Schmitz, U. Bars, R. Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KolnD-50679 Germany Department of Automation and Applied Informatics Budapest University of Technology and Economics Goldmann Gy. ter 3 BudapestH-1111 Hungary

For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the computational effort some commercial predictive control program packages offer the ability to reduce the number of points in both horizons but do not recommend how to select the points which have to be considered in the horizon of the control error and manipulated variable. in this work the authors introduce an optimal choice not only of the horizon lengths itself but also for the strategy of reducing the number of points in the horizons. A genetic optimization algorithm was used both for the search for the optimal length of the horizons and for the best allocation of the points in the horizons. The results of the optimization process where used to deduct a simple rule. © 2003 IFAC.

关键词： Genetic algorithms

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