串并联微动机构的运动学分析

本文提出了一种新颖的用于微动操作的“串并联”机构，利用矢量运算的方法，简便地分析了它的运动学特性，阐述了此机构的一些特点，并根据讨论的结果，设计出了微操作手的样机。     

MARKOV DECISION PROCESSES WITH UNCERTAIN TRANSITION RATES: SENSITIVITY AND MAX HYPHEN MIN CONTROL

Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain transition rates represented as compact sets. We first consider the problem of sensitivity analysis where the aim is to quantify the range of uncertainty of the average per‐unit‐time reward given the range of uncertainty of the transition rates. We then develop solution techniques for the problem of obtaining the max‐min optimal policy, which maximizes the worst‐case average per‐unit‐time reward. In each of these problems, we distinguish between systems that can have their transition rates chosen independently and those where the transition rates depend on each other. Our solution techniques are applicable to Markov decision processes with fixed but unknown transition rates and to those with time‐varying transition rates.     

马尔可夫平稳随机域模型与遥感图象空间邻域结构信息分析

遥感图象记录的是景物对电磁波的反射与辐射能量及其空间分布信息。对一个象元的判别不仅取决于其光谱值,还取决于该象元的空间位置及与其它象元的关系,即邻域结构关系。本文主要讨论遥感图象空间邻域结构信息分析方法。首先,提出了三种图象平面点阵平稳马尔可夫模型,并论证了相应的计算方法;然后给出了模型中相关参数的最小二乘估计;基于给出的三种模型,我们采用联合概率密度函数作为准则函数,并利用递归分类方法逐步改善近邻类别知识,从而改善邻域结构分类结果。试验证明了所提出的三种模型的有效性。     

使用“通信顺序进程”机制的神经网络仿真

本文引入了“通信顺序进程”的概念，同时介绍了一种实现环境－ＲＩＳＣ结构的Ｔｒａｎｐｕｔｅｒ处理器及Ｏｃｃａｍ并行化编程语言，并阐述了用“通信顺序进程”机制来仿真人工神经网络的方法。     

基于变权马尔可夫随机场的遥感图像变化检测

遥感图像变化检测通过分析比较不同时相所获得的遥感图像来获取变化信息,目前已经成为对地监测最有效的手段.针对传统马尔可夫随机场遥感图像变化检测算法的不足,提出一种基于变权马尔可夫随机场的遥感图像变化检测算法.该算法重新定义了马尔可夫能量函数的计算过程.实验结果表明该算法是一种有效、精确的变化检测算法.     

使用“通信顺序进程”机制的神经网络仿真

本文引入了“通信顺序进程”的概念，同时介绍了一种实现环境－ＲＩＳＣ结构的Ｔｒａｎｓｐｔｅｒ处理器及Ｏｃｃａｍ并行化编程语言，     

基于作者主题模型的遥感图像自动类别标注方法

以概率图模型为基础,提出一种基于作者主题模型ATM(Author Topic Model)的多光谱遥感图像类别标注方法。该方法采用了一种新的基于颜色和形状特征的描述符,并结合ATM对遥感图像进行类别标注。首先采用一组定义了语义鸿沟的图像作为训练图像,然后采用基于颜色和形状特征的视觉单词描绘训练图像,最后结合ATM对遥感图像进行类别标注。通过对实际的遥感图像进行类别标注验证,可以看出,所提出的基于ATM的遥感图像标注方法在区域类别较少的情况下具有较高的分类准确率。     

基于决策树的神经网络预测水泥熟料强度

测定水泥熟料强度的传统方法是实测水泥预制品在第3天与第28天的强度.为了简化并加快熟料强度的预测,设计了基于决策树的神经网络系统.该系统由两部分组成,先利用决策树确定水泥品种,再利用该种水泥样品的数据对神经网络进行训练.实验证明,经过训练的神经网络可以快速、准确地预测熟料的抗压、抗折强度.     

AN EMPIRICAL COMPARISON OF TECHNIQUES FOR HANDLING INCOMPLETE DATA USING DECISION TREES

Increasing the awareness of how incomplete data affects learning and classification accuracy has led to increasing numbers of missing data techniques. This article investigates the robustness and accuracy of seven popular techniques for tolerating incomplete training and test data for different patterns of missing data—different proportions and mechanisms of missing data on resulting tree-based models. The seven missing data techniques were compared by artificially simulating different proportions, patterns, and mechanisms of missing data using 21 complete datasets (i.e., with no missing values) obtained from the University of California, Irvine repository of machine-learning databases (Blake and Merz, 1998 Blake , C. L. and C. J. Merz . 1998 . UCI Repository of machine learning databases . University of California, Department of Information and Computer Science , Irvine , CA . (http:/www.ics.uci.edu/mlearn/MLRepository.html) . [Google Scholar]). A four-way repeated measures design was employed to analyze the data. The simulation results suggest important differences. All methods have their strengths and weaknesses. However, listwise deletion is substantially inferior to the other six techniques, while multiple imputation, that utilizes the expectation maximization algorithm, represents a superior approach to handling incomplete data. Decision tree single imputation and surrogate variables splitting are more severely impacted by missing values distributed among all attributes compared to when they are only on a single attribute. Otherwise, the imputation—versus model-based imputation procedures gave—reasonably good results although some discrepancies remained. Different techniques for addressing missing values when using decision trees can give substantially diverse results, and must be carefully considered to protect against biases and spurious findings. Multiple imputation should always be used, especially if the data contain many missing values. If few values are missing, any of the missing data techniques might be considered. The choice of technique should be guided by the proportion, pattern, and mechanisms of missing data, especially the latter two. However, the use of older techniques like listwise deletion and mean or mode single imputation is no longer justifiable given the accessibility and ease of use of more advanced techniques, such as multiple imputation and supervised learning imputation.     

利用云模型实现FSVM遥感影像分类的隶属度算法

针对遥感影像分类面临的数据边界模糊性以及遥感信息解译过程不确定性的问题,结合模糊支持向量机在分类应用中可以有效避免噪声样本干扰的特点,提出一种基于云模型求解模糊支持向量机隶属度的方法。该方法通过无需隶属度的逆向云算法输入样本的定量位置得到样本类别的数字特征,再根据正向云算法计算得到每个样本对其定性类别的隶属度。实验结果表明,采用基于云模型隶属度的模糊支持向量机对遥感影像的分类方法是可行的,并能够有效提高对遥感影像的分类精度。     

COMBINED QUALITATIVE/QUANTITATIVE SIMULATION MODELS OF CONTINUOUS-TIME PROCESSES USING FUZZY INDUCTIVE REASONING TECHNIQUES

Abstract

A new mixed quantitative and qualitative simulation methodology based on fuzzy inductive reasoning is presented. The feasibility of this methodology is demonstrated by means of a simple hydraulic control system. The mechanical and electrical parts of the control system are modeled using differential equations, whereas the hydraulic part is modeled using fuzzy inductive reasoning. The technique is described in detail in the first part of this paper. The example is shown in the second part of the paper. The mixed quantitative and qualitative model is simulated in ACSL, and the simulation results are compared with those obtained from a fully quantitative model. The example was chosen as a simple to describe, yet numerically demanding process whose sole purpose is to prove the concept. Several practical applications of this mixed modeling technique are mentioned in the paper. but their realization has not yet been completed