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Gene selection by using an improved Fast Correlation-Based Filter

Gene selection by using an improved Fast Correlation-Based F...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xue-Qiang Zeng Guo-Zheng Li Su-Fen Chen Computer Center University of Nanchang Nanchang China Department of Control Science & Engineering University of Tongji Shanghai China College of Information Engineering Nanchang Institute of Technology Nanchang China

Among various redundancy based gene selection methods, the Fast Correlation-Based Filter (FCBF) is one of the most effective. FCBF works in an iterative way, where one predominant feature is selected at each step and then some redundant features are removed by the selected one. However, the size of selected feature subset is not considered by FCBF, and weakly relevant features are too inclined to be eliminated. Aiming at this problem, this paper proposes a new approximate Markov blanket definition for FCBF, which strengthens the criterion for redundant features. Based on the new definition, the size of the selected feature set is used to adjust the criterion dynamically. Experimental results on several real gene data sets demonstrated the outstanding performance of the proposed algorithm compared with other several state-of-arts techniques.

关键词： Markov processes Redundancy Approximation algorithms Support vector machines Artificial neural networks Gene expression Bioinformatics

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Stochastic Differential Dynamic Programming

Stochastic Differential Dynamic Programming

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American control Conference

作者： Evangelos Theodorou Yuval Tassa Emo Todorov Computational Learning and Motor Control Lab Departments of Computer Science and Neuroscience University of Southern California Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel Department of Computer Science and Engineering and the Department of Applied Mathematics University of Washington Seattle WA

ISBN: (纸本)9781424474264

Although there has been a significant amount of work in the area of stochastic optimal control theory towards the development of new algorithms, the problem of how to control a stochastic nonlinear system remains an open research topic. Recent iterative linear quadratic optimal control methods iLQG [1], [2] handle control and state multiplicative noise while they are derived based on first order approximation of dynamics. On the other hand, methods such as Differential Dynamic Programming expand the dynamics up to the second order but so far they can handle nonlinear systems with additive noise. In this work we present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state and control multiplicative process noise, and proceed to derive the second-order expansion of the cost-to-go. We find the correction terms that arise from the stochastic assumption. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP.

关键词： dynamic programming theory of optimal control multiplicative noise Quartic stochastic derivative Additive noise correct entry dynamics Work area Handles

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Selection of optimal EEG channels for classification of signals correlated with alcohol abusers

Selection of optimal EEG channels for classification of sign...

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International Conference on Signal Processing Proceedings (ICSP)

作者： M. Alavash Shooshtari S. Kamaledin Setarehdan Biomedical Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Center of Excellence Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran

Many brain events and disorders can be detected by analyzing electroencephalograms (EEGs). Also the availability of quantitative biological markers that are correlated with qualitative psychiatric phenotypes helps us to utilize automated methods to diagnose and classify these phenotypes. One such a psychiatric phenotype is alcoholism. In this study a method to select an optimal subset of EEG channels for the purpose of practical classification of alcohol abusers from normal subjects is proposed, which is based on combination of model-based spectral analysis and correlation matrices. The EEG signals were recorded when the subjects were represented with single trial visual stimuli. The proposed method proved successful in selecting an optimum number of channels which achieved acceptable average classification accuracy.

关键词： Electroencephalography Support vector machines Correlation Visualization Accuracy Feature extraction Brain modeling

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Parameter identification of a linear permanent magnet motor using particle swarm optimization

Parameter identification of a linear permanent magnet motor ...

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7th Annual International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2010

作者： Therdbankerd, Tithiwat Sanposh, Peerayot Chayopitak, Nattapon Fujita, Hideaki Department of Electrical Engineering Intelligent Mechatronics Automation Robotics and Control Laboratory Kasetsart University 50 Phahonyothin Rd. Ladyao Jatujak Bangkok 10903 Thailand Industrial Control and Automation Laboratory National Electronics and Computer Technology Center 112 Thailand Science Park Phahonyothin Rd. Klong 1 Klong Luang Pathumthani 12120 Thailand Department of Electrical and Electronic Engineering Tokyo Institute of Technology S3-18 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan

ISBN: (纸本)9789746724913

Accurate and effective parameter identification is an important engineering task in high performance control system design. One emerging approach to effectively identify such nonlinear or dynamic unknown parameters is to use Particle Swarm Optimization (PSO) algorithm. Linear Permanent Magnet (LPM) motor is a high performance actuator employed in many applications that require direct linear motion without mechanical transmission for high acceleration and accurate positioning. Therefore, accurate motor parameters are necessary to effectively control the LPM motors. This paper proposes a simple PSO based method with chirp inputs to identify the LPM motor's parameters. The simulations and experiments are conducted to verify the results and determine the effectiveness of the proposed method.

关键词： Parameter estimation

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Iterative learning control of a class of fractional order nonlinear systems

Iterative learning control of a class of fractional order no...

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IEEE International Symposium on Intelligent control (ISIC)

作者： Yan Li Hyo-Sung Ahn YangQuan Chen School of Control Science and Engineering Shandong University Shandong China Gwangju Institute of Science and Technology Gwangju South Korea Center for Self-Organizing and Intelligent Systems of Department of Electrical and Computer Engineering Utah State University Logan UT USA

This paper firstly addresses the convergence analysis of iterative learning control of a class of fractional order nonlinear systems using the generalized Gronwall-Bellman lemma. Detailed problem definition and conver... 详细信息

关键词： Nonlinear systems Convergence Simulation Fractional calculus Linear systems Differential equations Laplace equations

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On the Infinite Horizon Constrained Switched LQR Problem

On the Infinite Horizon Constrained Switched LQR Problem

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2010 49th IEEE Conference on Decision and control

作者： Maximilian Balandat Wei Zhang Alessandro Abate Department of Electrical Engineering and Information Technology Technische Universitat Darmstadt Darmstadt Germany Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Delft Center for Systems and Control Technische Universiteit Delft Delft The Netherlands

ISBN: (纸本)9781424477456

This paper studies the Discrete-Time Switched LQR problem over an infinite time horizon subject to polyhedral constraints on state and control input. The overall constrained, infinite-horizon problem is split into two subprob-lems: (i) an unconstrained, infinite-horizon problem and (ii) a constrained, finite-horizon one. We derive a stationary suboptimal policy for problem (i) with analytical bounds on its optimality, and develop a formulation of problem (ii) as a Mixed-Integer Quadratic Program. By introducing the concept of a safe set, the solutions of the two subproblems are combined to achieve the overall control objective. It is shown that, by proper choice of the design parameters, the error of the overall sub-optimal solution can be made arbitrarily small. The approach is tested through a numerical example.

关键词： infinite horizon design parameter discrete-time numerical example optimality

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Impulse response invariant discretization of a generalized commensurate fractional order filter

Impulse response invariant discretization of a generalized c...

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World Congress on Intelligent control and Automation (WCICA)

作者： Yan Li Hu Sheng YangQuan Chen School of Control Science and Engineering Shandong University Jinan China Department of Electronic Engineering Dalian University of Technology Dalian China Center for Self-Organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering Utah State University Logan UT USA

In this paper, we derive the impulse response of a generalized commensurate fractional-order filter of the form (s 2α +as α +b) -γ , where a, b ≥ 0, α ∈ (0, 1] and γ ∈ (0, 1). The asymptotic properties of the impulse responses are obtained. Moreover, based on the derived analytical impulse response, we show how to perform the discretization of the above fractional-order filter. Finally, a number of illustrated examples in time and frequency domains are provided as proofs of concepts.

关键词： Laplace equations Fractional calculus Frequency response Equations Time frequency analysis Time domain analysis

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Sensor data fusion using fuzzy control for VOR-based vision tracking system

Sensor data fusion using fuzzy control for VOR-based vision ...

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2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Hyun-il Kwon Jaehong Park Wonsang Hwang Jong-hyeon Kim Chang-hun Lee M. Latif Anjum Kwang-soo Kim Dong-il Cho Automation and Systems Research Institute and School of Electrical Engineering and Computer Science Inter-University Semiconductor Research Center Seoul South Korea Department of Control and Instrumentation Engineering Hanbat National University Daejeon South Korea

This paper presents a vision tracking system to achieve high recognition performance under dynamic circumstances, using a fuzzy logic controller. The main concept of the proposed system is based on the vestibulo-ocular reflex (VOR) and the opto-kinetic reflex (OKR) of the human eye. To realize the VOR concept, MEMS inertial sensors and encoders are used for robot motion detection. This concept turns the camera towards a selected target, counteracting the robot motion. Based on the OKR concept, the targeting errors are periodically compensated, using vision information. The fuzzy logic controller uses sensor data fusion to detect slip or collision occurrences. To calculate a heading angle of the camera accurately, the output of the fuzzy logic controller and the vision information from the camera are combined, using an extended Kalman filter. The proposed vision tracking system is implemented in a mobile robot and evaluated experimentally. The experimental results are obtained as the tracking and the recognition success rate using a mobile robot. The developed system achieved the excellent tracking and recognition performance during slip or collision occurrences under dynamic circumstances.

关键词： Cameras Mobile robots Robot vision systems Machine vision Fuzzy logic

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Analyzing effect of distraction caused by dual-tasks on sharing of brain resources using SOM

Analyzing effect of distraction caused by dual-tasks on shar...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yu-Kai Wang Nikhil R. Pal Chin-Teng Lin Shi-An Chen Department of Computer Science and Brain Research Center National Chiao Tung University Taiwan Electronics and Communication Sciences Unit Indian Statistical Institute India Department of Electrical and Control Engineering and Brain Research Center National Chiao Tung University Taiwan

Drivers' distraction is widely recognized as a leading cause of car accidents. To investigate the distracting effect of dual-tasks involving driving and answering mathematical equations in the stimulus onset asynchrony (SOA) conditions, we design five different cases: two cases involving single-tasks and three cases involving dual-tasks. We have found that there is no statistically significant change in the behavioral data among the three dual-tasks. This raises an important question - is there any detectable effect of the dual tasks on the brain waves? To answer this, we use the Self-Organizing Map (SOM) to recognize the changes, if any, in the Electroencephalography (EEG) dynamics associated with such dual-tasks. Our SOM analysis based on independent components corresponding to EEG signals extracted from Frontal and Motor areas revealed that single- and dual-tasks have distinguishable signatures in the EEG signals. Specifically, each of the two single-task conditions is clustered in a distinct spatial area of the map. Two of the dual-tasks also exhibit distinct spatial clusters, while the third case although shows differences from the other two, the neurons corresponding to this case are sub-clustered reflecting the fact that different subjects may give different priorities to the tasks when confronted with two tasks simultaneously. SOM-based exploratory analysis reveals the existence of distinct EEG signatures among the distracting and non-distracting tasks, although there is no any noticeable difference in the behavioral data among these cases.

关键词： Electroencephalography Neurons Feature extraction Equations Mathematical model Brain Accuracy

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Friction feedforward compensation and position feedforward in CNC machines

Friction feedforward compensation and position feedforward i...

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7th Annual International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2010

作者： Suteeratanapirom, Wijin Rattanawaorahirankul, Rapeepong Laosapan, Chutima Sanposh, Peerayot Chayopitak, Nattapon Tungpimolrut, Kanokvate Kunieda, Hiroaki TAIST Tokyo Tech ICTES Program Department of Electrical Engineering Kasetsart University 50 Phahonyothin Rd. Ladyao Jatujak Bangkok 10903 Thailand Intelligent Mechatronics Automation Robotics and Control Laboratory Department of Electrical Engineering Kasetsart University 50 Phahonyothin Rd. Ladyao Jatujak Bangkok 10903 Thailand Industrial Control and Automation Laboratory National Electronics and Computer Technology Center 112 Thailand Science Park Phahonyothin Rd. Klong 1 Klong Luang Pathumthani 12120 Thailand Department of Electrical and Electronic Engineering Tokyo Institute of Technology S3-65 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan

ISBN: (纸本)9789746724913

This research proposes a method to eliminate friction effect and reduced tracking error on CNC milling machines. A CNC milling machine has many connected mechanical components which have friction effects such as ball screws and rails. To control servo motors which are used to drive X-Y table in CNC milling machine, the conventional PID controller is widely used. In this research, Panasonic motor driver is set to the torque control mode. To compensate the friction effects, the friction feedforward compensation method is proposed. Moreover, to reduce the position tracking error, position feedforward controller is used. The simulation and implementation of position control in one axis of PID with Friction Feedforward Compensation and Position Feedforward controller (FFC-PFC) are presented.

关键词： Friction

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