基于粗糙集的规则的挖掘

随着计算机技术的发展，急剧产生海量的数据。如何从这些数据中提取有用的信息是一个重要的问题。一种新的数据分析方法——粗糙集理论被提出。该理论在分类的意义下定义了模糊性和不确定性的概念，是一种处理不确定和不精确问题的新型数学工具。文中首先对近年兴起的粗糙集的基本理论进行了讨论，在此基础上运用粗糙集理论对从数据库中规则的挖掘方法进行了研究。并通过一个实例详细地说明了具体挖掘过程，该实例说明了基于粗糙集进行规则的挖掘是较简单的。     

广义双线性系统的残差产生器设计

针对具有不可测外界干扰和有界控制输入的广义双线性系统,提出了一种用于故障检测的未知输入残差产生器的设计方法。首先,通过非奇异线性变换,将广义双线性系统分解成动态和静态两个子系统,设计了未知输入残差产生器,给出了未知输入残差产生器存在的充分必要条件,并基于Lyapunov稳定理论,分析了所设计的未知输入残差产生器的指数稳定性。然后,针对未知输入残差产生器中的待定参数,基于线性矩阵不等式技术和广义逆理论,给出了未知参数的求解过程,该求解方法不需要对待求参数进行参数化的过程,易于计算。     

决策表的一种知识约简与规则获取方法

粗糙集理论是一种新型的数据挖掘和决策分析方法,利用粗糙集理论进行决策表的知识约简与决策规则挖掘已经成为研究热点。文中介绍了粗糙集的基本理论,在此基础上运用该理论对从决策表中获取最小规则进行了研究,提出了决策表约简的启发式方法,并通过一个具体实例详细说明了决策规则获取过程,实例分析表明了其有效性。     

基于相容关系的基因选择方法

有效的基因选择是对基因表达数据进行分析的重要内容。粗糙集作为一种软计算方法能够保持在数据集分类能力不变的基础上,对属性进行约简。由于基因表达数据的连续性,为了避免运用粗糙集方法所必需的离散化过程带来的信息丢失,将相容粗糙集应用于基因的特征选取,提出了基于相容关系的基因选择方法。首先,通过i检验对基因表达数据进行排列,选择评分靠前的若干基因;然后,通过相容粗糙集对这些基因进一步约简。在两个标准的基因表达数据上进行了实验,结果表明该方法是可行性和有效性的。     

Geometry optimization of a slender cantilever beam subjected to lateral buckling

The paper deals with the geometry optimization of a slender cantilever beam subjected to a concentrated force acting at the free end. The two-parametric mathematical model of lateral torsional buckling is based on the Bernoulli–Euler beam theory and is given in dimensionless form. The optimization procedure is performed using the optimal control theory and the relation between state and adjoint variables is presented. The boundary value problem derived from the optimization procedure is solved numerically and compared to solutions obtained via an alternative optimization approach called sequential approximate optimization.     

Fuzzy observer design using linear matrix inequalities for fuzzy closed-loop control systems

Linear observers play an important role in modern control theory and practice. A systematic design method of fuzzy observers would be important for fuzzy control as well. The fuzzy observer is designed by solving linear matrix inequalities (LMI) that represent control performance such as disturbance rejection and robust stability. Our approach in designing the fuzzy observer is based on the LMI formulation of the stability conditions for closed-loop Takagi-Sugeno (T-S) fuzzy systems, when states are not available for measurement of feedback. We present a new approach, which is to design an observer based on fuzzy implications, with fuzzy sets in the antecedents, and an asymptotic observer in the consequents. Each fuzzy rule is responsible for observing the states of a locally linear subsystem. An example illustrates the design procedure.     

Data Refinement of Remote Procedures

Recently the action systems formalism for parallel and distributed systems has been extended with the procedure mechanism. This gives us a very general framework for describing different communication paradigms for action systems, e.g. remote procedure calls. Action systems come with a design methodology based on the refinement calculus. Data refinement is a powerful technique for refining action systems. In this paper we will develop a theory and proof rules for the refinement of action systems that communicate via remote procedures based on the data refinement approach. The proof rules we develop are compositional so that modular refinement of action systems is supported. As an example we will especially study the atomicity refinement of actions. This is an important refinement strategy, as it potentially increases the degree of parallelism in an action system. Received February 1999 / Accepted in revised form July 2000     

Real-time neural-network midcourse guidance

The approximation capability of artificial neural networks has been applied to the midcourse guidance problem to overcome the difficulty of deriving an on-board guidance algorithm based on optimal control theory. This approach is to train a neural network to approximate the optimal guidance law in feedback form using the optimal trajectories computed in advance. Then the trained network is suitable for real-time implementation as well as generating suboptimal commands. In this paper, the advancement of the neural-network approach to the current level from the design procedure to the three-dimensional flight is described.     

Economic plant-wide control design with backoff estimations using internal model control

Economic optimal operation typically involves operating as close as possible to the active constraints. However, in the presence of disturbances it is necessary to back-off from the constraints in order to avoid violating them. The backoff approach aims at selecting the control structure that minimizes the economic loss associated with the required constraint backoffs. This paper revisits the backoff approach and proposes a framework for estimating the constraint backoffs based on well-known elements of internal model control (IMC) theory, such as an automatic procedure for tuning the IMC low-pass filters, a stability condition, and an uncertainty representation based on diagonal input multiplicative uncertainty. Since the constraint backoffs are estimated using a linear dynamic model, the inclusion of input multiplicative uncertainty allows introducing conservatism in the estimation of the backoffs, which is required in order to avoid constraint violations. A forced-circulation evaporator benchmark problem is used to illustrate the approach.     

Inverse method based on modal analysis for characterizing the constitutive properties of thick composite plates

This paper presents a powerful method for evaluating the constitutive properties of composite laminates through a mixed numerical–experimental identification procedure based on both the extracted mode shapes and the corresponding natural frequencies of the structure. The modal quantities are measured with a precise contact-free experimental technique and extracted numerically with an accurate shell element derived from the higher order shear deformation theory. The elastic properties are estimated with a nonlinear least squares algorithm applied to modal identification criteria. With this novel approach of combining frequency and mode based error norms, the proposed identification method allows an accurate identification of both in-plane and transverse elastic properties with a single non-destructive test. The effectiveness of the procedure is highlighted by test cases on moderately thick to thick plates.     

A balanced solution of a fuzzy soft set based decision making problem in medical science

The purpose of this paper is two folded. Firstly, the concept of mean potentiality approach (MPA) has been developed and an algorithm based on this new approach has been proposed to get a balanced solution of a fuzzy soft set based decision making problem. Secondly, a parameter reduction procedure based on relational algebra with the help of the balanced algorithm of mean potentiality approach has been used to reduce the choice parameter set in the parlance of fuzzy soft set theory and it is justified to the problems of diagnosis of a disease from the myriad of symptoms from medical science. Moreover the feasibility of this proposed method is demonstrated by comparing with Analytical Hierarchy Process (AHP), Naive Bayes classification method and Feng's method.     

Minimal model generation for refined answering of generalized queries in disjunctive deductive databases

Generalized queries are defined as sets of clauses in implication form. They cover several tasks of practical importance for database maintenance such as answering positive queries, computing database completions and integrity constraints checking. We address the issue of answering generalized queries under the minimal model semantics for the class of disjunctive deductive databases (DDDBs). The advanced approach is based on having the query induce an order on the models returned by a sound and complete minimal model generating procedure. We consider answers that are true in all and those that are true in some minimal models of the theory. We address the issue of answering positive queries through the construction of the minimal model state of the DDDB, using a minimal model generating procedure. The refinements allowed by the procedure include isolating a minimal component of a disjunctive answer, the specification of possible updates to the theory to enable the derivability of certain queries and deciding the monotonicity properties of answers to different classes of queries.     

Generalized Jeffrey’s rule of conditioning and evidence combining rule for a priori probabilistic knowledge in conditional evidence theory

In Bayesian probabilistic approach for uncertain reasoning, one basic assumption is that a priori knowledge about the uncertain variable is modeled by a probability distribution. When new evidence representable by a constant set is available, the Bayesian conditioning is used to update a priori knowledge. In the conventional D-S evidence theory, all bodies of evidence about the uncertain variable are imprecise and uncertain. All bodies of evidence are combined by so-called Dempster’s rule of combination to achieve a combined body of evidence without considering a priori knowledge. From our point of view, when identifying the true value of an uncertain variable, Bayesian approach and evidence theory can cooperate to deal with uncertain reasoning. Firstly all imprecise and uncertain bodies of evidence about the uncertain variable are fused to achieve a combined evidence based on a priori knowledge, then the a posteriori probability distribution is achieved from a priori probability distribution by conditioning on the combined evidence. In this paper we firstly deal with the knowledge updating problem where a priori knowledge is represented by a probability distribution and new evidence is represented by a random set. Then we review the conditional evidence theory which resolves the knowledge combining problem based on a priori probabilistic knowledge. Finally we discuss the close relationship between knowledge updating procedure and knowledge combining procedure presented in this paper. We show that a posteriori probability conditioned on fused body of evidence satisfies the Bayesian parallel combination rule.     

Transformation and Normal Vector Calculation of Parametrically Defined Surfaces Based on Dual Vectors and Screw Theory: Application to Phong's Shading Model

This paper presents a new approach for the transformation and normal vector calculation algorithms of parametrically defined surfaces via dual vectors and line transformations. The surface is defined via dual points, the transformation is performed by rotations and translations based on screw theory while normal vector calculation is utilized for shading based on Phong's illumination model. The main benefit of this approach lies into the compactness of the surface's representation since geometrical characteristics, such as tangent vectors, that are necessary for shading algorithms, are included within its definition. An extensive comparison is performed between the proposed approach and the traditional homogeneous model, presenting the merits of our approach. Analytical and experimental determination of the computational cost via computer implementation of 3D surface transformation and shading is presented. Point‐based methods for the representation, transformation and shading of parametrically defined surfaces are compared to the introduced line‐based methods (dual quaternions and dual orthogonal matrices). It is shown that the simplified rendering procedure of 3D objects, is considerably faster using screw theory over the traditional point‐based structures.     

How to Strip a Model to Its Essential Elements

Econometric models can be considered as a quantified representation of current economic theory. The paper suggests a procedure to check the correspondence between theory and practice. After an introduction about methodological aspects, a model of the Canadian economy is reduced by appropriate aggregation of the variables. Its condensed structure is then analysed, in order to investigate especially some noteworthy economic circuits such as financial flows and investment. The whole approach is strictly qualitative, based exclusively on the causal structure of the model. The results remain therefore consistent with any quantification of the model.     

Active diagnosis of discrete-event systems

The need for accurate and timely diagnosis of system failures and the advantages of automated diagnostic systems are well appreciated. However, diagnosability considerations are often not explicitly taken into account in the system design. In particular, design of the controller and that of the diagnostic subsystem are decoupled, and this may significantly affect the diagnosability properties of a system. The authors present an integrated approach to control and diagnosis. More specifically, they present an approach for the design of diagnosable systems by appropriate design of the system controller. This problem, which they refer to as the active diagnosis problem, is studied in the framework of discrete-event systems (DESs); it is based on prior and new results on the theory of diagnosis for DESs and on existing results in supervisory control under partial observations. They formulate the active diagnosis problem as a supervisory control problem where the legal language is an “appropriate” regular sublanguage of the regular language generated by the system. They present an iterative procedure for determining the supremal controllable, observable, and diagnosable sublanguage of the legal language and for obtaining the supervisor that synthesizes this language. This procedure provides both a controller that ensures diagnosability of the closed-loop system and a diagnoser for online failure diagnosis. The procedure can be implemented using finite-state machines and is guaranteed to converge in a finite number of iterations. The authors illustrate their approach using a simple pump-valve system     

基于三次样条函数拟合的过程神经元网络训练

过程神经元网络的提出为大样本识别问题开辟了新途径，但其训练方法目前主要基于权函数正交基展开。这种方法基函数个数选取目前尚无理论依据。提出了基于三次样条函数拟合的过程神经元网络训练方法。首先将样本函数、过程神经元权函数的离散化数据拟合成分段表示的三次样条函数，然后计算样本样条函数与权值样条函数乘积在给定采样区间上的积分，并将此积分值提交给网络的过程隐层神经元，输出层由普通神经元组成。三次样条函数具有很好的光滑性、可积性、阶数低、参数少等优点，有效地简化了网络的时空聚合运算。实验表明该方法是可行的。     

A meshless FPM model for solving nonlinear material problems with proportional loading based on deformation theory

In this work a methodology of meshless finite points method for the analysis of nonlinear material problems with proportional loading based on deformation theory is presented.In finite points method the approximation around each point is obtained by using weighted least square techniques. The discrete system of equation is constructed by means of a point collocation procedure. The non-dependence on a mesh or integration procedures is an important aspect which transforms the finite point method in a truly meshless technique.Hencky’s total deformation theory and an elastic approach is used on the determination of stress–strain fields. This approach introduces the concept of effective material properties which are considered as spatial field variables and to be functions of equilibrium stress state and material properties.The present results are in good agreement with those obtained by nonlinear finite element method and previous work in this meshless context. Nevertheless the present methodology is based on a strong formulation, keeping the meshless characteristics of FPM.     

Procedure for manipulator inverse kinematics computation and proper pseudoinverse perturbation

In this article, an efficient procedure for the proper pseudoinverse perturbation and the numerical inverse kinematics computation of robot manipulators is presented. The approach is based on solving a linear system of equations and using an original scheme for the appropriate perturbation of the pseudoinverse matrix in the next iteration. The resultant algorithm is tested on the simulation of an industrial robot manipulator. From the results obtained, it is observed that the proposed approach compares favorably with the approaches using a Gaussian elimination procedure and with pseudoinverse robustness based on a manipulability measure.     

Congestion Control as a Constrained State Regulation Problem

This paper investigates the field of congestion control in networks. It provides a new approach based on viability theory and feedback regulation. Basically it is shown that feedback control laws which lead to congestion avoidance can be derived by a selection procedure of a certain set-valued map. Finally, the paper studies existence of stabilizing congestion feedback by using Lyapunov theory along with some facts of set-valued analysis.