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New Synchronization Algorithm and Analysis of Its Convergence Rate for Clock Oscillators in Dynamical Network with Time-Delays

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Journal of Beijing Institute of Technology 2010年第1期19卷 58-65页

作者：甘明刚于淼陈杰窦丽华 School of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

New synchronization algorithm and analysis of its convergence rate for clock oscillators in dynamical network with time-delays are presented.A network of nodes equipped with hardware clock oscillators with bounded drift is ***,a dynamic synchronization algorithm based on consensus control strategy,namely fast averaging synchronization algorithm （FASA）,is presented to find the solutions to the synchronization *** FASA,each node computes the logical clock value based on its value of hardware clock and message *** goal is to synchronize all the nodes＇ logical clocks as closely as ***,the convergence rate of FASA is analyzed that proves it is related to the bound by a nondecreasing function of the uncertainty in message delay and network ***,FASA＇s convergence rate is proven by means of the robust optimal ***,several practical applications for FASA,especially the application to inverse global positioning system （IGPS） base station network are ***,numerical simulation results demonstrate the correctness and efficiency of the proposed *** FASA with traditional clock synchronization algorithms （CSAs）,the convergence rate of the proposed algorithm converges faster than that of the CSAs evidently.

关键词： clock synchronization convergence rate dynamical network robust optimal design

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Mobile robot's electronic compass calibration based on modified Fourier Neural Network

Mobile robot's electronic compass calibration based on modif...

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IEEE Conference on Robotics, Automation and Mechatronics

作者： Gong Kun Deng Fang Ma Tao Gong Kun is with School of Automation Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Deng Fang is with School of Automation Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Ma Tao is with School of Automation Beijing Institute of Technology and Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China

In order to improve the precision of the azimuth measured by mobile robot's electronic compass, this paper proposes a new calibration method based on Fourier Neural Network trained by Modified Particle Swarm Optimization (MPSO-FNN). This method makes use of Fourier Neural Network (FNN) to establish the error compensation model of electronic compass's azimuth, and introduces Modified Particle Swarm Optimization (MPSO) algorithm to optimize the weights of neural network. Thus the comparatively accurate error model of azimuth is obtained to compensate the output of electronic compass. This method not only has strong nonlinear approximation capability, but also overcomes the neural networks' shortcomings which are too slow convergence speed, oscillation, and easy to fall into local optimum and sensitive to the initial values. Experimental results demonstrate that after calibrated by this method, the range of azimuth error reduces to -0.35°~0.70° from -3.4°~25.2°, and the average value of absolute error is only 0.30°.

关键词： Compass Azimuth Biological neural networks Particle swarm optimization Training Accuracy Automation

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T-S fuzzy observer for nonlinear magnetic levitation system with uncertain variable friction coefficient

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 114-118页

作者： Zhou, Li-Jie Guo, Shu-Li Gao, Zhe School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex system Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China

An adaptive fuzzy observer for nonlinear magnetic levitation system with uncertain friction coefficient is proposed. First, a T-S fuzzy model of the nonlinear magnetic levitation system is proposed. An observer is given by LMI method and the Lyapunov method. Through adding an auxiliary variable, it relaxes the constraint of the design of observer. The proposed approach is a more relaxed condition than others. Simulation results show the effectiveness of this approach. The results show that the position of magnetic levitation system is estimated effectively with unknown friction coefficient. There is some reference value for a kind of nonlinear systems with unknown parameter.

关键词： Lyapunov methods

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An algorithm based on ODLLTSA and SVM classier for door plate number recognition

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 789-793页

作者： Ma, Li-Ling Ji, Li-Jun Wang, Jun-Zheng School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education Beijing Institute of Technology Beijing 100081 China

A new method is proposed, through combining the algorithm of orthogonal discriminant linear local tangent space alignment (ODLLTSA) and the support vector machine (SVM), to improve the accuracy of recognizing door plate numbers. The feature of door plate characters is first extracted by the ODLLTSA and then this extracted feature is used to train the SVM classifier. Finally, the new plate characters are classified by the trained SVM. Using the algorithm, a high recognition rate can be achieved. Experimental results show that this method is effective and robust in the real applications.

关键词： Support vector machines

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Optical encoder fault diagnosis based on characteristics of data and fuzzy logic

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 876-882页

作者： Deng, Fang Chen, Jie School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China

The fault diagnosis for a class of widely used digital parallel output optical encoder were focused. After definition of the optical encoder, the main features of the optical encoder's output data were analyzed. A fault diagnosis method which did not rely on the system model where optical encoder used was proposed. The changes of optical encoder's output data were analyzed. Then, the inherent characteristics were calculated. The fuzzy logic was utilized to determine the fault type and locate the fault location. Theoretical analysis and experimental results show that this method can diagnose and isolate optical encoder fault accurately without disassembly.

关键词： Fuzzy logic

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Simulation scenario design method and its application based on conceptual models of mission space

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2011年第SUPPL. 1期42卷 1096-1100页

作者： Gao, Bing-Zhi Wu, Di Ge, Xiong Ban, Xiao-Juan School of Computer Science and Electronics University of Science and Technology Beijing Beijing 100083 China Key Laboratory of Complex System Intelligent Control and Decision of Education Ministry Beijing Institute of Technology Beijing 100081 China

A simulation system scenario design method was proposed for the space-based information simulation system supporting anti-earthquake rescue. This method is based on the conceptual models of the mission space method including the formal description of simulation aim, scale, static data, dynamic data set and simulation play. A simulation scenario creator was developed to realize these functions and prove the usability of these models.

关键词： Earthquakes

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Object tracking based on multi-bandwidth mean shift with convergence acceleration

Object tracking based on multi-bandwidth mean shift with con...

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2nd International Conference on Image Analysis and Signal Processing, IASP'2010

作者： Bin, Zhou Jun-Zheng, Wang Wei, Shen Key Laboratory of Complex System Intelligent Control and Decision School of Automatic Control Beijing Institute of Technology Beijing China

ISBN: (纸本)9781424455553

A multi-bandwidth based tracking algorithm was proposed to search for the global kernel mode when the probability density has multiple peak modes. Firstly, a monotonically decreasing sequence of bandwidths was fixed according to the target scale. At each bandwidth, using mean shift to And out the maximum probability, and starting the next iteration at the previous convergence location. Finally, the best optimal mode could be obtained at the last bandwidth. To accelerate the convergence, over-relaxed strategy was introduced to enlarge the step size. Under the convergence rule, the learning rate was adaptively adjusted by Bhattacharyya coefficients of consecutive iteration convergence. The experimental results show that the proposed multi-bandwidth mean shift tracker is robust in high-speed object tracking, and perform well in occlusions. The adaptive over-relaxed strategy is effective to lower the convergence iterations by enlarging the step size. © 2010 IEEE.

关键词： Bandwidth

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An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization

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Science China(Information Sciences) 2010年第5期53卷 980-989页

作者： XIN Bin 1,2 ? , CHEN Jie 1,2* , PENG ZhiHong 1,2 & PAN Feng 1,2 1 School of Automation, Beijing Institute of Technology, Beijing 100081, China 2 key laboratory of complex system intelligent control and decision, Ministry of Education, Beijing 100081, China 1. School of Automation Beijing Institute of Technology Beijing 100081 China2. Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China

This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). The hybrid optimizer achieves on-the-fly adaptation of evolution methods for individuals in a statistical learning way. Two primary parameters for the novel algorithm including its learning period and population size are empirically analyzed. The dynamics of the hybrid optimizer is revealed by tracking and analyzing the relative success ratio of PSO versus DE in the optimization of several typical problems. The comparison between the proposed DEPSO and its competitors involved in our previous research is enriched by using multiple rotated functions. Benchmark tests involving scalability test validate that the DEPSO is competent for the global optimization of numerical functions due to its high optimization quality and wide applicability.

关键词： global optimization statistical learning differential evolution particle swarm optimization hybridization adaptation rotated function

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Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation

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电子学报(英文版) 2010年第3期19卷 451-456页

作者： LI Bo LIU Wenju DOU Lihua School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling. Finite Gaussian mixture model is usually used in practice and the selection of number of mixture components is a significant problem in its application. For example, in image segmentation, it is the donation of the number of segmentation regions. The determination of the optimal model order therefore is a problem that achieves widely attention. This paper proposes a degenerating model algorithm that could simultaneously select the optimal number of mixture components and estimate the parameters for Gaussian mixture model. Unlike traditional model order selection method, it does not need to select the optimal number of components from a set of candidate models. Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it select the correct model order in a different way that needs less operation times and less sensitive to the initial value of EM. The experimental results show the effectiveness of the algorithm.

关键词：高斯混合模型图像分割无监督学习应用概率密度函数椭球等高分布模型估计高斯密度

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Identification of interval models for a class of uncertain systems via linear programming

Identification of interval models for a class of uncertain s...

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作者： Zhang, Guozhu Chen, Jie Li, Zhiping Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology School of Automation Beijing 100081 China

ISBN: (纸本)9781424474264

The interval models of uncertain plants are frequently used in the field of robust control. In this paper, a novel interval model identification method based on linear programming is proposed. By certain prepossessing on the measured data of the plant, the interval model identification problem is transformed into several linear programming problems, which can be easily solved through various algorithms. We have proved that the resultant interval model has a known error bound, provided that certain conditions on the measured data are satisfied. To demonstrate its effectiveness, the proposed method is applied to identify the interval model of a servo system with variant load and inertia. © 2010 AACC.

关键词： Linear programming

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