具有非参数化不确定系统的无偏辨识

本文研究了具有参数和非参数不确定性系统的集员辨识问题：分析表明利用我们在文（５）中提出的ＢＥＬＳ方法可以消除集员辨识中观测噪声引起的偏差，文中通过对系统输入数据的预滤波将已知零点嵌入系统，利用这些零点提供的信息将观测噪声引起的辨识偏差予以消除。     

基于Laplace逼近Gaussian过程的指节图像中层偏移测度特征学习

在人机协调装配中,为了准确描述手部位姿,需要精确的指节图像特征提取与识 别。为了丰富手部信息,提出了基于 Laplace 逼近 Gaussian 过程的多分类算法,以实现基于手 部图像的指节识别。在类别信息无关联的假设基础上,将中层偏移测度特征的学习转化为对随 机量的学习;然后通过分析二值多分类高斯场上的后验计算,给出了基于 Laplace 逼近 Gaussian 过程的多分类高斯过程学习算法;通过构造中层随机信息的正定核函数,给出了基于 Laplace 的多分类高斯过程预测算法。最后,利用中层数据的分布学习与预测算法进行了指节图像训练 学习和固定阈值的图像识别。识别结果显示,该方法具有一定的指节识别能力。     

应用非参数统计理论辨识一类非线性系统

应用非参数统计理论提出了含已知结构输入非线性的线性系统的开环辨识算法;给出了对算法的理论分析结果;并针对典型系统进行了仿真研究.     

一类非线性动态系统的非参数GFRF模型辨识

本文对一类用多项式描述的非线性动态系统提出使用ＧＦＲＦ模型类的非参数辨识算法，这种算法的显著特点是需要很小的计算量和存储空间，而且辨识精度较高，仿真结果表明这种非参数模型辨识算法是有效的，而且由辨识方法获得的模型一般具有很好的泛化能力，因而是一种具有重要应用前景的实用方法 。     

基于拉普拉斯算法的图像锐化算法研究和实现

该文提出了一种基于拉普拉斯算法的图像锐化方法,并在DSP上实现其算法。首先研究拉普拉斯算子锐化图像的基本原理,并推导出图像锐化的拉普拉斯算子。其次,根据拉普拉斯算子,在CCS2软件上运用C语言编写主函数和读取图像数据的Read-image子函数、初始化图像的InitImage子函数和对图像锐化的计算Laplace子函数等子函数来实现基于拉普拉斯算法的图像锐化程序。最后采用三副模糊图像验证图像锐化的效果。比较实验结果,可知运用该算法锐化处理的图像比原来图像清晰。     

Optimal Control of Random Processes

Recent years have witnessed considerable interest in the problem of optimal control of diverse processes. And this is not surprising, since any theory explaining a phenomenon must, in the last analysis, reduce to a theory for controlling it. The achievements of contemporary computational technology provide favorable conditions in this regard. With the aid of a digital computer, we can not only “simulate” the consequences of arbitrary decisions but also find most suitable (optimal) decisions. This enables us to realize very complex control algorithms within an acceptable time.     

Online Algebraic Identification Approach of Continuous Linear Time Delay Systems

In this paper, we propose an online algebraic method to identify linear continuous time delay process from step response, in presence of unknown initial state and constant load disturbance. The identification mechanism is split into two sequential steps. The time delay and dynamic of the plant is estimated firstly through a spectral formulation. In the next stage, the transient regime is deduced from a linear regression. To improve the robustness of the proposed method, a local convolution by sigmoid function is proposed. Simulation results are provided, at last, to show the effectiveness of our conceived approach.     

Migration Processes I: The Continuous Case

In this paper the general concept of a migration process (MP) is introduced; it involves iterative displacement of each point in a set as function of a neighborhood of the point, and is applicable to arbitrary sets with arbitrary topologies. After a brief analysis of this relatively general class of iterative processes and of constraints on such processes, we restrict our attention to processes in which each point in a set is iteratively displaced to the average (centroid) of its equigeodesic neighborhood. We show that MPs of this special class can be approximated by reaction-diffusion-type PDEs, which have received extensive attention recently in the contour evolution literature. Although we show that MPs constitute a special class of these evolution models, our analysis of migrating sets does not require the machinery of differential geometry. In Part I of the paper we characterize the migration of closed curves and extend our analysis to arbitrary connected sets in the continuous domain (R^m) using the frequency analysis of closed polygons, which has been rediscovered recently in the literature. We show that migrating sets shrink, and also derive other geometric properties of MPs. In Part II we will reformulate the concept of migration in a discrete representation (Z^m).     

Random Search in Problems of Optimization,Identification and Training of Control Systems

The problem of adaptation of control systems (in the wide sense) reduces to finding the extremum of a functional that specifies the performance of the system to be adapted. We shall assume that the problem is parametrized, i.e., it reduces to finding parameters x1,…, xn,. of the adapting plant that extremize the functional Q = Q(X, A, B) (1.1)

under given constraints X∈G. where X = (x1,…, xn) is the vector of the parameters to be optimized, G is the set of admissible values of this vector, A = (a1,…,am) is the vector of input signals, and B = (b₁,…,b_k) is the vector of the system's response to the disturbances A and to E = (E1,…,Ep,), which is the vector of uncontrolled parameters of the system (noise, drift, etc.). The function B = F(A, X, E) is not known.     

非参数图像分割算法的研究及改进

对传统的Dirichlet过程混合(MDP)非参数算法进行改进,提出一种新的MDP非参数图像分割算法。引入马尔可夫随机场(MRF)空间领域关系,并将其作为空间先验约束条件对图像后验概率加以约束。该算法能够光滑图像中的边缘部分、控制分类数并加快收敛速度。实验结果表明,与传统算法相比,该算法的分割准确度较高。     

连续系统的模型参考自适应辨识新方法

１引言文［１］提出了将待辨系统以被控对象的形式嵌入到Ｎａｒｅｎｄｒａ等［２］的自适应控制结构中去的连续系统辨识方案,解决了传统的模型参考自适应辨识方法不能处理待辨系统不稳定或不具备良好暂态品质的问题．但它不能处理非最小相位系统,而且对于相对阶大于１并且传递函数分子阶次大于０的系统,因需要进行矩阵求逆而使运算复杂,精度降低．本文在文［１］的基础上,提出了将待辨系统与一个适当选择的相对阶为１的系统并联后作为被控对象嵌入到模型参考自适应控制系统（ＭＲＡＣＳ）中去的辨识方案,解决了上述问题。２辨识方案考…     

图像识别技术在光电自动定向系统中的应用

将模式识别技术引入到光电定向之中,采取中值滤波、基于灰度分布特征的选择性灰度拓展、动态阈值计算和带修正的模板匹配等算法,有效地消除了不定光照和随机背景的影响,解决了航天器光电定向中光学图像识别定位的难题;该方法具有算法稳定、识别率高、定位准确的优点,为实现光电定向的自动化提供了有益思路.     

复杂系统的非参数决策模型

本文针对烧结过程不确定性大,缺乏足够的结构信息,含有大时滞和变时滞等特点,提出用非参数决策“黑箱“模型来研究复杂系统参数间的制约关系。实例表明,所设计的分类器效果良好,推断烧结矿质量的正确率达８１．５％。该模式识别系统具有普遍的工程应用价值。     

Ergodic Theory and Arithmetical Simulation of Random Processes

The relationships between arithmetical simulation of random processes, ergodic theory, and optimization are analyzed. Some new results are considered and their possible applications to optimization problems are described.     

图像边缘检测Laplace算子扩展的讨论

针对边缘检测Laplace算子扩展的问题，提出一种扩展模板方法及扩展模板的权重设置规则。在VC＋＋环境下编程实现该方法，实验结果表明用这种方法进行图像边缘检测，效果更好。     

A Problem of Control of Random Processes

A control problem for a random process depending additively on the fractional Brownian motion with the Hurst parameter H (1/2, 1) is analyzed.     

Intrinsic Random Fields and Image Deformations

The stochastic structure of images, especially individual medical images as they are reconstructed nowadays from arrays of medical imaging sensors, is becoming steadily better understood. Less attention has been paid to the parallel notion of estimation error for the deformations that convey relations among these images, such as localized abnormality or growth prediction. The dominant current formalism for the biostatistics of deformations deals solely with the shape of a set of landmarks parameterizing the deformation, not otherwise with its behaviour inbetween the landmarks. This paper attempts to fit a rigorous stochastic model for a deformation between landmarks and to assess the error of the fitted deformation. The relation between two images is modelled as a stochastic deformation, i.e. as an identity map plus a stochastic process whose value at every point is a vector-valued displacement. There are two common strategies for fitting deformations given information at a set of landmarks. One involves minimizing a roughness penalty, e.g. for a thin-plate spline, and another involves prediction for a stochastic process, e.g. for a self-similar intrinsic random field. The stochastic approach allows parameter estimation and confidence limits for the predicted deformation. An application is presented from a study of breast images and how they deform as a function of the imaging procedure.     

Effective Image Segmentation with Flexible ICM-Based Markov Random Fields in Distributed Systems of Personal Computers

This paper presents the implementation of modified Markov Random Fields (MRFs) in distributed systems of personal computers. Gibbs Random Fields (GRFs) operating in the iterated conditional mode (ICM), modified to incorporate the flexibility of selecting from a continuum of configurations ranging from greater fidelity to the original image to more contextual influence (and enhanced smoothing), are presented, implemented in a distributed system of personal computers, and assessed for image segmentation. The characteristics of the distributed system, the message interchange mechanisms, the strategy for the implementation of the MRF, as well as the statistical characterization of the performance in terms of hardware utilization, bottlenecks and speed-up are presented and discussed. The results indicate that, despite their relative computational complexity, the developed concurrent system presents good potential for allowing MRFs to be executed in real-time for many applications in image processing and computer vision.