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International Conference on computer and Automation engineering, ICCAE

作者： Hossein Chegini Caro Lucas Science and research Branch/Computer Science Islamic Azad university of Tehran Tehran Iran Electrical and Computer Engineering Department Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran

ISBN: (纸本)9781424455690

The tolerance and non-stability in financial indexes make changes to other sub-systems like human resources, economics, factory productions and etc. Having underling knowledge and a model to simulate such systems obtains a fine vision to estimate further and calculate hard-decision making tasks before execution like: dept from banks, cash injecting and insurance services. Using Neuro-fuzzy networks are one of the most powerful tools for this estimation. The particular locally linear model type of these networks called LoLiMot are in interest because of their linear training and construction optimization. These network can be much efficient when be a recurrent network why can better capture the dynamism's order of dynamic processes. The Locally linear Neuro-Fuzzy model (LoLiMot) here is as basis for making recurrent. In this paper this network with a global state feedback is implemented and the accuracy and the results of this recurrent network on Dow Jones index as a financial time series are compared with the static LoLiMot. The obtained results were better.

关键词： Predictive models State feedback Neurons Output feedback Power system modeling Gaussian processes control engineering computing Process control Intelligent control computer science

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Global Stabilization of the Double Integrator System With Saturation and Delay in the Input

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IEEE Transactions on Circuits and Systems I: Regular Papers 2010年第6期57卷 1371-1383页

作者： Bin Zhou Guangren Duan Zongli Lin Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

This paper is concerned with the problem of global stabilizing a double integrator system subject to actuator saturation and delay in the input. Two solutions to the problem are proposed by using linear state feedback which are parameterized in a single scalar γ. The first solution is delay-dependent in the sense that the delay information is directly used in the feedback design. The second solution is delay-independent as the delay information is not directly used in the feedback. Ranges of the value of the free parameter γ are established within which the closed-loop system is globally asymptotically stable. A numerical example is used to illustrate the effectiveness of the proposed approach.

关键词： Delay control systems Actuators Systems engineering and theory Linear systems State feedback Nonlinear systems control design Degradation Regulators

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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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Feature selection and classification of breast cancer on dynamic Magnetic Resonance Imaging by using artificial neural networks

Feature selection and classification of breast cancer on dyn...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Farzaneh Keivanfard Mohammad Teshnehlab Mahdi Aliyari Shoorehdeli Ke Nie Min-Ying Su Electrical and Computer Engineering Department Khaje Nasir Toosi University of Technology Tehran Iran Electrical and Computer Engineering Department Advanced Process Automation and Control (APAC) Khaje Nasir Toosi University of Technology Tehran Iran Center for Functional Onco-Imaging and Department of Radiological Sciences University of California Irvine CA USA

In this paper, a new feature selection and classification methods based on artificial neural network are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI). The database including benign and malignant lesions is specified to select the features and classify with proposed methods. It is collected from 2004 to 2006. A forward selection method is applied to find the best features for classification. Moreover, artificial neural networks such as Multilayer Preceptron (MLP) neural network, Probabilistic Neural Network (PNN) and Generalized Regression Neural Network (GRNN) are applied to classify breast cancer into two groups; benign and malignant lesions. Training and recalling neural networks are obtained with considering four-fold cross validation.

关键词： Fires Entropy Correlation Artificial neural networks Lesions Jacobian matrices Kinetic theory

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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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Friction feedforward compensation and position feedforward in CNC machines

Friction feedforward compensation and position feedforward i...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Wijin Suteeratanapirom Rapeepong Rattanawaorahirankul Chutima Laosapan Peerayot Sanposh Nattapon Chayopitak Kanokvate Tungpimolrut Hiroaki Kunieda TAIST Tokyo Technology China Intelligent Mechatronics Automation Robotics and Control Laboratory Department of Electrical Engineering Kasetsart University Bangkok Thailand Industrial Control and Automation Laboratory National Electronics and Computer Technology Center Klong Luang Pathum Thani Thailand Department of Electrical and Electronic Engineering Tokyo Institute of Technology Meguro Tokyo Japan

ISBN: (纸本)9781424456062;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 computer numerical control Metalworking machines Servomotors Three-term control Fasteners Rails Servomechanisms Torque control Error correction

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A learning approach to optimize walking cycle of a passivity-based biped robot

A learning approach to optimize walking cycle of a passivity...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Nima Fatehi Masoud Asadpour Adel Akbarimajd Laila Majdi Mechatronic Group Electrical and Computer Engineering Faculty Islamic Azad University Qazvin Iran Control and Intelligent Processing Center of Excellence Electrical Engineering Faculty University of Tehran Tehran Iran ECE Group Faculty of Engineering University of Mohaghegh Ardabil Iran Mechanical Department Islamic Azad University Tehran Iran

A learning mechanism based on Powell's optimization algorithm is proposed to optimize walking behavior of a passivity based biped robot. To this end, a passivity-based biped robot has been simulated in MSC ADAMS and a control policy inspired from humanoid walking is adopted for a stable walking of the robot. Linear controllers try to control the joints of robot in each walking phase to implement the gait proposed by the control policy. Learning is employed using Powell's optimization algorithm to adjust the control parameters so that the robot enters to an optimum limit cycle in a finite time. The fitness function is defined to evaluate the robot's optimum behavior. The results are verified by simulations in SIMULINK+ADAMS.

关键词： Legged locomotion Optimization Hip Joints Foot Leg

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High performance vision tracking system for mobile robot using sensor data fusion with Kalman filter

High performance vision tracking system for mobile robot usi...

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2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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

This paper introduces a high performance vision tracking system for mobile robot using sensor data fusion. For mobile robots, it is difficult to collect continuous vision information due to robot's motion. To solve this problem, the proposed vision tracking system estimates the robot's position relative to a target and rotates the camera towards the target. This concept is derived from the human eye reflex mechanism, known as the Vestibulo-Ocular Reflex (VOR), for compensating the head motion. This concept for tracking the target results in much higher performance levels, when compared with the conventional method that rotates the camera using only vision information. The proposed system do not require heavy computing loads to process image data and can track the target continuously even during vision occlusion. The robot motion information is estimated using data from accelerometer, gyroscope, and encoders. This multi-sensor data fusion is achieved using Kalman filter. The proposed vision tracking system is implemented on a two-wheeled robot. The experimental results show that the proposed system achieves excellent tracking and recognition performance in various motion scenarios, including scenarios where camera is temporarily blocked from the target.

关键词： Robot sensing systems Target tracking Machine vision Kalman filters Cameras

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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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A statistical shape model using 2D-principal component analysis from few medical samples and its evaluation

A statistical shape model using 2D-principal component analy...

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International Conference on Software engineering and Data Mining (SEDM)

作者： Tomoko Tateyama Taishi Tanaka Shinya Kohara Amir Hossein Foruzan Akira Furukawa Yen-Wei Chen Intelligent Image Processing Laboratory College of Information Science and Engineering Ritsumeikan University Shiga Japan Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Radiology Shiga University of Medical Science Shiga Japan

ISBN: (纸本)9781424473243;9788988678220;9788988678213

Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling method using 2D PCA. The 3D shape is represented as a matrix by spherical parameterization. The experiments showed that our proposed method can reconstruct statistical shape model with good generalization even using fewer samples.

关键词： Biomedical imaging Shape control Biomedical engineering Principal component analysis Liver Information analysis Educational institutions Azimuth Covariance matrix Image analysis

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