游志胜
模式识别、图像处理、信息融合及其在空中管制和地面智能交通系统的应用。
个性化签名
- 姓名:游志胜
- 目前身份:
- 担任导师情况:
- 学位:
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学术头衔:
博士生导师
- 职称:-
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学科领域:
计算机应用
- 研究兴趣:模式识别、图像处理、信息融合及其在空中管制和地面智能交通系统的应用。
游志胜,1945年9月生,四川成都人。1968年四川大学物理系本科毕业,从事电子技术工作;1978至1981年为四川大学无线电系硕士生,1981—1983年在美国密执安州立大学计算机系作访问学者。1983年起在四川大学计算机系从事教学科研工作。现为四川大学计算机学院教授、博士生导师,四川大学国防科技研究院副院长、教育部现代交通管理工程研究中心主任。
主要研究方向是模式识别、图像处理、信息融合及其在空中管制和地面智能交通系统的应用。主持国家和军队有关重大项目二十余项。作为第一完成人获得国家科技进步一等奖一项,二等奖二项,省部级一等奖四项,对我国民航、军航空管系统现代化作出重要贡献,被中国民航总局聘为特聘专家,国务院中央军委国家空管委办公室评为“全国空管先进个人”。游志胜是国家有突出贡献的中青年专家、四川省学术和技术带头人、全国优秀留学归国人员、五一劳动奖章获得者、全国先进工作者,并担任教育部科技委学部委员、中国图像图形学会副理事长。于2006年获得香港何梁何利奖和科学中国人2006年度人物,同时主持的《CDZS系列空中交通管制中心系统》项目获国家科技进步奖一等奖。
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407
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成果数
10
【期刊论文】The optimality for the distributed Kalman filtering fusion with feedback☆
游志胜, Yunmin Zhu a, *, , Zhisheng You b, Juan Zhao c, Keshu Zhang c, X. Rong Li d
Automatica 37(2001)1489-1493,-0001,():
-1年11月30日
A rigorous performance analysis is dedicated to the distributed Kalman filtering fusion with feedback for distributed recursive state estimators of dynamic systems. It is shown that the Kalman filtering track fusion formula with feedback is, like the track fusion without feedback, exactly equivalent to the corresponding centralized Kalman filtering formula. Moreover, the so-called P matrices in the feedback Kalman filtering at both local trackers and fusion center are still the covariance matrices of tracking errors. Although the feedback here cannot improve the performance at the fusion center, the feedback does reduce the covariance of each local tracking error. The above results can be extended to a hybrid track fusion with feedback received by a part of the local trackers.
Kalman filtering, Distributed track fusion, Feedback, Performance analysis
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【期刊论文】Enumeration ofbinary orthogonal arrays ofstrength 1
游志胜, Jian-Zhou Zhang a, ∗, , Zhi-Sheng You a, Zheng-Liang Li b
Discrete Mathematics 239(2001)191-198,-0001,():
-1年11月30日
A k2m
Binary orthogonal arrays, Enumeration, Inclusion-exclusion principle, Edge-induced subgraph, Connected component
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引用
游志胜, Yiguang Liu a, b, *, Zhisheng You b, Liping Cao c
Neurocomputing 67(2005)369-383,-0001,():
-1年11月30日
Efficient computation of the largest eigenvalue and the smallest eigenvalue of a real symmetric matrix is a very important problem in engineering. Using neural networks to complete these operations is in an asynchronous manner and can achieve high performance. This paper proposes a concise functional neural network (FNN) expressed as a differential equation and designs steps to do this work. Firstly, the mathematical analytic solution of the equation is received, and then the convergence properties of this FNN are fully gained. Finally, the computing steps are designed in detail. The proposed method can compute the smallest eigenvalue and the largest eigenvalue whether the matrix is non-definite, positive definite or negative definite. Compared with other methods based on neural networks, this FNN is very simple and concise, so it is very easy to realize.
Functional neural network, Real symmetric matrix, Eigenvalues, Eigenvectors
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【期刊论文】Dynamical behaviors of Hopfield neural network with multilevel activation functions
游志胜, Yiguang Liu a, b, *, Zhisheng You b, Liping Cao c
Chaos, Solitons and Fractals 25(2005)1141-1153,-0001,():
-1年11月30日
When the activation function possesses multilevel property, the Hopfield neural network has some novel dynamical behaviors, and it is worthwhile to study. First, some properties about the activation function are obtained, on this foundation, some theoretical analysis about the quasi-equilibrium points has been made. From local and global view, some theorems about the boundedness are presented. Finally, two theorems about the first derivative of trajectory with respect to time are found, the first theorem indicates that the trajectory cannot keep increasing or decreasing for time t>to, the second theorem is about the complete stability of the trajectory.
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32浏览
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引用
游志胜, 王明辉, 赵荣椿, 聂健荪
电子学报,2000,28(6):13~15,-0001,():
-1年11月30日
本文提出一个基于数据关联性能评价的优化跟踪门算法,并通过它来减少跟踪门内来自非本目标的回波,最终达到提高多目标多传感器跟踪系统性能的目的。与最优跟踪门相比,经理论分析和仿真数据表明,本算法有效改善了系统的性能,尤其在强干扰、高虚警的情况下更为明显。
多目标跟踪, 数据关联, 跟踪门, 优化算法
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34浏览
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游志胜, 王明辉, 赵荣椿, 张建州, 冯子亮
电子学报,2000,29(12):45~47,-0001,():
-1年11月30日
本文针对仅能获得关于目标角度信息的被动式传感器、且允许传感器漏检、虚警、以及监视空域目标位置和数量未知的静态数据关联问题,提出了一个基于空间交点属性强度的快速数据关联算法。与现有算法相比,本方法较好地改进了数据关联算法的精度和运算速度。最后,通过仿真实验给以验证。
多目标跟踪, 数据关联, 被动式传感器
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33浏览
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41下载
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引用
游志胜, 赵娟, 马洪, 梅田三千雄
电子学报,2001,29(9):1157~1160,-0001,():
-1年11月30日
本文基于多尺度卡尔曼滤波方法来估计淹没在加性高斯白噪声中的分形布朗运动,针对每一尺度,给出了相应的动态系统参数和运动模型方程以及更精确的估计算法。并与多尺度维纳滤波进行了对比,计算机仿真结果证明了其优越性。
分形随机信号, 分形布朗运动, 1/, f过程, 卡尔曼滤波, 小波变换
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52浏览
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13下载
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游志胜, 张可数, 马洪, 梅田三千雄
电子学报,2000,29(9):1161~1163,-0001,():
-1年11月30日
本文用基于最小均方误差准则的最优门限方法估计叠加高斯白噪声的分形布朗运动,并给出其离散小波变换分解级数确定方法。与多尺度维纳滤波相比,本方法不需估计1/f类分形信号的方差,且其离散小波变换分解级数可预先确定。因此有着更好的实用性和可操作性。
分形信号, 分形布朗运动, 1/, f过程, 信号估计, 小波变换
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32浏览
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【期刊论文】强干扰环境下性能优化的相互作用多模型-概率数据关联算法1)
游志胜, 王明辉, 赵荣椿, 张建州
自动化学报,2001,27(2):267~271,-0001,():
-1年11月30日
在大区域空中交通管制ATM(Air Traffic Manage)调度多批次飞机时,由于各种干扰和虚警的存在,致使相互作用多模型-概率数据关联算法(IMM-PDA)的性能下降。为此,提出一个新颖的算法,即利用某些先验概率知识构造一个判断回波有效性的函数,通过该函数来估计无效回波、并将其排除在外,从而改善了算法的性能。最后,通过仿真给以验证。
空中交通管制(, ATM), ,, 多目标跟踪,, 相互作用多模型-概率数据关联算法(, IMM-PDA), 算法
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64浏览
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游志胜, 刘怡光+, 曹丽萍, 蒋欣荣
软件学报,2005,16(6):1064~1072,-0001,():
-1年11月30日
While using continuous time neural network described by the E.Oja. learning rule (Oja-N) for computing real symmetrical matrix eigenvalues and eigenvectors, the initial vector must be on Rn unit hyper-sphere surface, otherwise, the network may produce limit-time overflow. In order to get over this defect, a new neural network (lyNN) algorithm is proposed. By using the analytic solution of the differential equation of lyNN, the following results are received: If initial vector belongs to a space corresponding to certain eigenvector, the lyNN equilibrium vector will converge in this space; If initial vector does not fall into the space corresponding to any eigenvector, the equilibrium vector will belong to the space spanned by eigenvectors corresponding to the maximum eigenvalue. The initial vector maximum space for the lyNN equilibrium vector will fall into space spanned by eigenvectors corresponding to any eigenvalue received. If the initial vector is perpendicular to a known eigenvector, so is the equilibrium vector. The equilibrium vector is on the hyper-sphere surface decided by the initial vector. By using the above results, a method for computing real symmetric matrix eigenvalues and eigenvectors using lyNN is proposed, the validity of this algorithm is exhibited by two examples, indicating that this algorithm does not bring about limit-time overflow. But for Oja-N, if the initial vector is outside the unit hyper-sphere and the matrix is negatively determinant, the neural network will consequentially produce limit-time overflow. Compared with other algorithms based on optimization, lyNN can be realized directly and its computing weight is lighter.
neural network, symmetric matrix, eigenvalue, eigenvector, limit-time overflow
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