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检索条件"机构=Lab. of Intelligent Systems and Control Engineering Guilin University of Electronic Technology"

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EHM Based Neural Model for Hysteresis

EHM Based Neural Model for Hysteresis

引用

IEEE International Conference on Networking, Sensing and control

作者： Lianwei Ma Yonghong Tan Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China Department of Automation Shanghai JiaoTong University Shanghai China

This paper proposes a neural model for hysteresis. A method of continuous transformation is used to construct an elementary hysteresis model (EHM), which implements a one-to-one mapping between the input space and the output space of the hysteresis. The output of the EHM is used as one of the input signals of the neural network (NN) to approximate the behavior of hysteresis. A parabolic factor is introduced to improve the modeling performance for the major and minor loops of the hysteresis

关键词： Magnetic hysteresis Neural networks Piezoelectric actuators Magnetic levitation Degradation System performance System identification Oscillators Neurons intelligent systems

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A hybrid algorithm of facial feature point location based on improved MR-ASM and AAM

A hybrid algorithm of facial feature point location based on...

引用

8th International Conference on Signal Processing, ICSP 2006

作者： Sicong, Zhang Lifang, Wu Xiaoguang, He Jie, Tian School of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Key Lab. of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences P.O.Box 2728 Beijing 100080 China

ISBN: (纸本)0780397371

Accurate and robust location of feature point is a difficult and complicated issue in face recognition. This paper proposes a facial feature point location algorithm based on improved Multi-Resolution- Active Shape Models (MR-ASM) and Active Appearance Models (AAM). There are three main achievements of our algorithm: 1, we unify MR-ASM and AAM together to improve facial feature point location. 2, AAM is improved so that it has the same implementation framework as ASM. 3, the MR-ASM is adjusted to figure out more robust parameters. Experimental results prove that our algorithm is more accurate than traditional MR-ASM. © 2006 IEEE.

关键词： Face recognition

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Neural modeling for hysteresis in piezoelectrical actuator

Neural modeling for hysteresis in piezoelectrical actuator

引用

5th International Conference on control and Automation, ICCA'05

作者： Zhao, Xinglong Tan, Yonghong A0303222 Department of Automation Shanghai Jiaotong University Shanghai 200030 China Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic Technology Guilin 541004 China

ISBN: (纸本)0780391381

A neural network based method of modeling hysteresis in piezo-electrical actuator is proposed. In this method, a hysteretic operator is introduced to transform the multi-valued mapping of hysteresis into a one-to-one mapping so that neural networks can be utilized to approximate the characteristic of hysteresis. The result of modeling the hysteresis exists in a piezo-electrical actuator using the proposed method is finally presented. © 2005 IEEE.

关键词： Hysteresis

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Weighted support vector machine for classification

Weighted support vector machine for classification

引用

IEEE systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on systems, Man and Cybernetics

作者： Du, Shu-Xin Chen, Sheng-Tan National Key Lab of Industrial Control Technology Institute of Intelligent Systems and Decision Making Zhejiang University Hangzhou 310027 China School of Mechnical Electronic Engineering Xi'dian University Xi'an 710071 China

In the standard support vector machines for classification, the use of training sets with uneven class sizes results in classification biases towards the class with the large training size. The main causes lie in that the penalty of misclassification for each training sample is considered equally. Weighted support vector machines for classification are proposed in this paper where penalty of misclassification for each training sample is different. By setting the equal penalty for the training samples belonging to same class, and setting the ratio of penalties for different classes to the inverse ratio of the training class sizes, the obtained weighted support vector machines compensate for the undesirable effects caused by the uneven training class size, and the classification accuracy for the class with small training size is improved. But this improvement is obtained at the cost of the possible decrease of classification accuracy for the class with large training size and the possible decrease of the total classfication accuracy. Two weighted support vector machines, namely weighted C-SVM and v-SVM, corresponding to C-SVM and V-SVM are given respectively. Experimental simulations on breast cancer diagnosis show the effectiveness of the proposed methods. © 2005 IEEE.

关键词： Database systems

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Neural modeling for hysteresis in piezoelectrical actuator

Neural modeling for hysteresis in piezoelectrical actuator

引用

International Conference on control and Automation (ICCA)

作者： Xinglong Zhao Yonghong Tan Department of Automation Shanghai Jiaotong University Shanghai China Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China

关键词： Hysteresis Piezoelectric actuators Neural networks Distribution functions intelligent networks Degradation System performance Automation lab.ratories intelligent systems

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Adaptive H∞ RBFN tracking control for nonlinear systems with unknown hysteresis

Adaptive H∞ RBFN tracking control for nonlinear systems wit...

引用

Proceedings of the 2004 IEEE International Symposium on intelligent control - 2004 ISIC

作者： Tong, Zhao Tan, Yonghong Department of Automation Shanghai Jiaotong University Shanghai 20030 China Lab. of Intelligent Systems and Control Engineering Guilin University of Electronic Technology Guilin 541004 China

In this paper, a radial basis function network (RBFN) based adaptive H ∞ control scheme for nonlinear systems with unknown hysteresis nonlinearity is developed. This scheme applies the method of pseudo-control to the design of the control strategy for the systems with hysteresis that can not be measured directly. For the uncertainty of unknown hysteresis, H ∞ optimal control technique based on RBF neural network is utilized. Therefore, the tracking error of the system is suppressed to a prescribed small region. Finally, the effectiveness of the proposed control scheme is illustrated through simulation. © 2004 IEEE.

关键词： Tracking (position)

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Adaptive control based on neural estimation for systems with unknown hysteresis

Adaptive control based on neural estimation for systems with...

引用

IEEE Conference on control technology and Applications (CCTA)

作者： Chuntao Li Yonghong Tan Nanjing University of Aeronautics and Astronautics Nanjing China Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China

ISBN: (纸本)0780386337

A neural network based adaptive control scheme for systems with unknown hysteresis is proposed. In this scheme, an adaptive controller based on the proposed neural model is presented for a class of single-input nonlinear systems preceded by unknown hysteresis non-linearity. In order to handle the case where the output of hysteresis is immeasurable, the neural network model is utilized to estimate the influence of hysteresis. Based on the model-based estimation, the controller can compensate for hysteresis effect on the performance of the system.

关键词： Adaptive control Hysteresis Neural networks Programmable control Feedback loop Inverse problems Mathematical model Nonlinear control systems Nonlinear systems intelligent control

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Adaptive H/sup /spl infin// RBFN tracking control for nonlinear systems with unknown hysteresis

Adaptive H/sup /spl infin// RBFN tracking control for nonlin...

引用

IEEE International Symposium on intelligent control (ISIC)

作者： Zhao Tong Yonghong Tan Department of Automation Shanghai Jiaotong University Shanghai China Laboratory of Intelligent Systems and Control Engineering Guilin University of Electronic and Technology Guilin China

ISBN: (纸本)0780386353

A radial basis function network (RBFN) based adaptive H/sup /spl infin// control scheme for nonlinear systems with unknown hysteresis nonlinearity is developed. This scheme applies the method of pseudo-control to the design of the control strategy for the systems with hysteresis that cannot be measured directly. For the uncertainty of unknown hysteresis, the H/sup /spl infin// optimal control technique based on RBF neural network is utilized. Therefore, the tracking error of the system is suppressed to a prescribed small region. Finally, the effectiveness of the proposed control scheme is illustrated through simulation.

关键词： Hysteresis control systems Radial basis function networks Adaptive systems Programmable control Adaptive control Nonlinear control systems Nonlinear systems Optimal control Neural networks

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Smith predictor based neural controller with time-delay estimation 15th

Smith predictor based neural controller with time-delay esti...

引用

15th World Congress of the International Federation of Automatic control, 2002

作者： Tan, Yonghong Karim, M. Nazmul Lab. of Intelligent Systems and Control Engineering Guilin University of Electronic Technology Guilin541004 China Dept. of Chemical and Bioresource Engineering Colorado State University Fort CollinsCO80523 United States

ISBN: (纸本)9783902661746

A neural control strategy for nonlinear processes with time-variant time-delay is proposed in this paper. In this strategy, a dynamic neural network based nonlinear Smith predictor is constructed to compensate for the effect of time-delay of a class of nonlinear processes. An on-line optimizing controller is illustrated based on the neural Smith predictor. It is known that the performance of the Smith predictor may be deteriorated if the time-delay of the process changes with time. In order to improve the performance of the Smith predictor, a time-delay adaptation mechanism is introduced into the control structure to track the variation of the time-delay. The simulation, comparing with the classical Smith predictive control, on a continuous-stirred-tank-reactor (CSTR), where the time-delay of the manipulating flow changes with time, is used for the test. Copyright © 2002 IFAC.

关键词： Delay control systems

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Neural network based time-delay estimation for nonlinear dynamic systems 15th

Neural network based time-delay estimation for nonlinear dyn...

引用

15th World Congress of the International Federation of Automatic control, 2002

作者： Tan, Yonghong Su, Chun-Yi Karim, Naz Lab. of Intelligent Systems and Control Engineering Guilin University of Electronic Technology Guilin541004 China Dept. of Mechanical Eng. Concordia University Montreal Canada Dept. of Chemical Eng. Colorado State University Fort Collins United States

ISBN: (纸本)9783902661746

The estimation for the nonlinear dynamic system with time-varying input time-delay is an important issue for system identification. In order to estimate the dynamics of the process, a dynamic neural network with external recurrent structure is applied to the modelling procedure. In the case where time-delay is time varying, a useful way is to develop on-line time-delay estimation mechanisms to track the input time-delay variation. In this paper, two schemes respectively called direct as well as indirect time-delay estimators are proposed. Finally, two numerical examples are illustrated for the test of the proposed methods. Copyright © 2002 IFAC.

关键词： Time delay

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