一种基于熵的OBDD变量排序算法

有序二叉决策图（OBDD）是一种有效表示布尔函数的数据结构,其大小依赖于所采用的变量序。熵是定量描述布尔函数中变量重要性的一种方法。基于变量的熵值分析了高质量变量序的特征,给出了一种基于熵的OBDD变量排序算法。实验结果表明：该算法与模拟退火算法和遗传算法结果相当。时间仅为相应算法的80．84％和29．79％。     

基于重量分析的OBDD变量排序算法

有序的二叉判决图（ＯＢＤＤ）是布尔表达式的一种有效表示方法，但它的体积对变量排序具有较强的依赖性。本文提出一种电路结构图，并在此基础上定义了原始输入重量和节点重量等参数，并建立了用重量分析来指导的ＯＢＤＤ变量排序算法。由于从考虑变量对输出函数的影响出发与从考虑ＯＢＤＤ节点共享性出发对变量排序的要求不同，本文分别设计了两类算法。实验结果表明，本文对大多数标准电路变量排序的效果都优于国际上的同类算法，     

同一变量排序下的多OBDD合并算法

有序决策图（OBDD）是一种用于表示布尔表达式的数据结构,并在许多领域得到了广泛应用。在分布式或者动态环境下,利用已知布尔表达式的OBDD构造目标布尔表达式的OBDD是一个决定实际问题解决效率的关键问题。基于Shannon分解原理提出了一个同一变量排序下的OBDD合并算法。该算法首先建立目标布尔表达式的表存储模型,然后按照变量排序的逆序,依次处理各个变量,并且合并取值相同的行,直到所有变量处理完毕。     

均匀空间色差度量的矢量形态学图像处理

彩色图像矢量形态学处理中,针对矢量排序准则存在模糊性和片面性等缺点,提出一种新的矢量排序方法。首先根据在均匀的Lab空间中,颜色之间视觉上的差距与颜色坐标上的欧几里得距离成正比的特点,将量化后的色差大小作矢量排序准则;然后利用四元数法建立彩色图像模型和形态学结构元素模型,在此基础上定义新的彩色形态学基本运算子;最后研究了矢量形态学图像处理算法,对比了本文方法和HSV矢量排序法的应用效果。实验结果表明,本文方法能较好地用于提取图像边缘和去除椒盐噪声。     

An efficient optimality criterion method for natural frequency constrained structures

This paper presents an optimality criterion method for the design of structural systems under natural frequency constraints. A new recursive formula is derived. The method is applied to a variety of beams and frames. Results indicate the efficiency and simplicity of the method in locating the optimal design as compared to existing methods. The method avoids the scaling of design variable often required in optimality criterion methods and can treat large nonstructural masses on the structures.     

Minimization Problems for Parity OBDDs

Parity Ordered Binary Decision Diagrams (⊕OBDDs) are a data structure for boolean functions that extends the well-known OBDDs and reduces the representation size for several functions. Both data structures share the problem that the representation size strongly depends on the chosen variable order. For ⊕OBDDs the number of edges and thus the representation size is also influenced by the choice of the basis of the represented vector space. In this paper the hardness of some minimization problems for ⊕OBDDs is proven, namely, that there is no polynomial time approximation scheme for minimizing the number of nodes by choosing the variable order and for minimizing the number of edges, where the variable order may be changed or is fixed, unless P=NP.     

基于小波神经网络的加工过程自适应控制

把信息熵、小波分析和神经网络相结合,提出了基于小波神经网络的加工过程自适应控制系统及其自适应控制算法。提出并定义了广义熵方误差函数,在理论上证明了广义熵方误差函数的有效性。用广义熵方误差函数准则取代BP算法的均方误差准则,用自适应地搜索小波基函数和自适应地调整小波的尺度参数、平移参数和神经网络权值的方法对参数变化的切削力进行在线控制。仿真结果表明,该系统响应快,无超调,比传统的加工过程神经网络自适应控制具有更好的控制效果。     

Determining equivalent values for possibilistic variables

We discuss the nature of possibilistic uncertainty. Two approaches to the determination of the equivalent value for a possibilistically uncertain variable are introduced. These approaches are then generalized to yield a parameterized class of functions for obtaining the equivalent value of a possibilistic variable. A novel aspect of this class is that it is parameterized by a weak ordering. A discussion to try to understand the semantics of this parameterizing ordering is included. A method is suggested for obtaining the weak ordering directly from a scalar value. We briefly look at the issue of variance in possibilistic uncertainty.     

Fuzzy set theoretic approach of assigning weights to objectives in multieriteria decision making

A rational procedure is developed for ranking the multiobjectives of a vector maximum problem, thereby leading to proper assignment of weights to the objectives. To achieve this, we determine the range of each decision variable from the constraints set and transform them into the unit interval. After scaling the decision variables, we scale down the objectives also into the unit interval. Following Yager (1981), an ordering function of the objectives is determined. This ordering function value is nothing but the strength of preference inherent in the objectives. Accepting the strength of preference, we obtain the weights for the respective objectives. A numerical example is given to illustrate the procedure.     

Integrating Standard Dependency Schemes in QCSP Solvers

Quantified constraint satisfaction problems (QCSPs) are an extension to constraint satisfaction problems (CSPs) with both universal quantifiers and existential quantifiers.In this paper we apply variab...     

The correctness problem: evaluating the ordering of binary features in rankings

In machine learning, feature ranking (FR) algorithms are used to rank features by relevance to the class variable. FR algorithms are mostly investigated for the feature selection problem and less studied for the problem of ranking. This paper focuses on the latter. A question asked about the problem of ranking given in the terminology of FR is: as different FR criteria estimate the relationship between a feature and the class variable differently on a given data, can we determine which criterion better captures the “true” feature-to-class relationship and thus generates the most “correct” order of individual features? This is termed as the “correctness” problem. It requires a reference ordering against which the ranks assigned to features by a FR algorithm are directly compared. The reference ranking is generally unknown for real-life data. In this paper, we show through theoretical and empirical analysis that for two-class classification tasks represented with binary data, the ordering of binary features based on their individual predictive powers can be used as a benchmark. Thus, allowing us to test how correct is the ordering of a FR algorithm. Based on these ideas, an evaluation method termed as FR evaluation strategy (FRES) is proposed. Rankings of three different FR criteria (relief, mutual information, and the diff-criterion) are investigated on five artificially generated and four real-life binary data sets. The results indicate that FRES works equally good for synthetic and real-life data and the diff-criterion generates the most correct orderings for binary data.     

A generalized model for jointly determining the optimal ordering point and the optimal number of minimal repairs before replacement

Many maintenance policies have assumed that whenever a unit is to he replaced, a new unit is immediately available. However, if the procurement lead time is not negligible, an ordering policy should be considered that determines when to order a spare and when to replace the operating unit after it has begun operating. A generalized model is presented to jointly determine the optimal ordering point and the optimal number of minimal repairs before replacement. By introducing costs due to ordering, repairs, shortage and holding, the expected cost per unit time in the long run is derived as a criterion of optimality, and the optimal ordering point and the optimal number of minimal repairs before replacement are sought by minimizing that cost. Various special cases are discussed. Finally, a numerical example is given.     

An extension of singular value analysis for assessing manipulated variable constraints

A new approach called feasible output radius analysis for linear or linearised models is introduced to address the problem of scaling dependency. This problem arises when assessing the effect of manipulated variable constraints (MVCs) on the closed-loop performance of chemical processes prior to carrying out control designs. The new indicators, and can be used to rank alternative control schemes on the basis that the larger and , the better the closed-loop performance in the presence of control constraints. These indicators are determined from extending the concept of the ‘feasible output amplitude region’ and are independent of the input scaling chosen. Theoretical analysis shows that this method is an extension of the more traditional singular value analysis approach and is more flexible in dealing with various kinds of manipulated variable constraints. A case study, i.e. a two-CSTR process, is investigated using the new method. Via the case study, some superior characteristics of the new technique are demonstrated, such as ease of calculation, and flexibility in coping with different kinds of constraints.     

一种清浊音判决的参考标准及一种新算法研究

目前的清浊音判决方法比较多,但没有一种方法是十分完美的,也没有一种完善的清浊音判决方法评价标准,为了检验一种新的算法对清浊音判决的有效性,提出了一种基于短时平均幅度和短时过零率相结合的清浊音判决参考标准,同时提出了一种基于残差信号短时自相关法的清浊音判决方法,并用此参考标准检验对该算法进行评价,通过matlab程序对算法进行仿真实验,实验结果表明这个参考标准可以很好地检验一种算法的有效性。     

Size of ordered binary decision diagrams representing threshold functions

An ordered binary decision diagram (OBDD) is a graph representation of a Boolean function. In this paper, the size of ordered binary decision diagrams representing threshold functions is discussed. We consider two cases: the case when a variable ordering is given and the case when it is adaptively chosen. We show 1) O(2^n/2) upper bound for both cases, 2) Ω(2^n/2) lower bound for the former case and 3) Ω(n2^√n/2) lower bound for the latter case. We also show some relations between the variable ordering and the size of OBDDs representing threshold functions.     

On the influence of the variable ordering for algorithmic learning using OBDDs

OBDDs with a fixed variable ordering are used successfully as data structure in experiments with learning heuristics based on examples. In this paper, it is shown that, for some functions, it is necessary to develop an algorithm to learn also a good OBDD variable ordering. There are functions with the following properties. They have OBDDs of linear size for optimal variable orderings. But for all but a small fraction of all variable orderings one needs large size to represent a list of randomly chosen examples. These properties are shown for simple functions like the multiplexer and the inner product.     

Dynamic programming and convex clustering

In this paper the notion of convexity of clusterings for the given ordering of units is introduced. In the case when at least one (optimal) solution of the clustering problem is convex, dynamic programming leads to a polynomial algorithm with complexityO(kn ³). We prove that, for several criterion functions, convex optimal clusterings exist when dissimilarity is pyramidal for a given ordering of units.This research was supported in part by the Research Council of Slovenia.     

前向神经网络信赖域学习算法的研究

本文介绍了引入信赖域优化理论解决神经网络中学习问题的新算法,提出了计算有效信赖域步方法,以保证信赖域算法的正确性,采用变系数方法避免了信赖域半径自适应调整过程中不稳定和低效的问题。实验表明,信赖域学习算法优于变尺度算法。     

Input selection for parameter identification in discrete systems

In this paper the problem of selecting an optimal input for identifying an unknown parameter of a known discrete system by observing its output in the presence of Gaussian noise is considered. The system is assumed to be a generalized discrete system in which the inputs and possible parameter values are members of a finite set. The criterion for the optimal input is defined as that which maximizes the probability of correctly determining the true parameter value from a multiple hypothesis test. Although the above criterion totally orders the set of inputs, it is a difficult task to select the best inputs. Some theorems are presented which yield a partial ordering whose extension is the desired total ordering. In the special case of strong noise, it is shown that the ordering of inputs can be related to the perimeter in the output vector space. The results of the paper are applicable to the selection of preset input lengths or to adaptive identification.