Structural modification of systems using discretization and generalized sampled-data hold functions

This paper investigates the decentralized control of LTI continuous-time plants using generalized sampled-data hold functions (GSHF). GSHFs can be used to modify the structure of the digraph of the resultant discrete plant, by removing certain interconnections in the discrete-time equivalent model to form a hierarchical system model of the plant. This is a new application of discretization and has, as its motivation, the design of decentralized controllers using centralized methods.     

Simultaneous pole assignment in linear periodic systems byconstrained structure feedback

The authors present methods for pole assignment by feedback with constrained structure in linear periodic systems using generalized sampled-data hold functions (GSHF). Constrained structure is taken to mean that the input of each channel is restricted to depend only on the measurements of some specific output channels. The basic idea of GSHF control is to use sample and hold, but to consider the hold function as a design parameter. Four strategies are proposed for closing a control loop using GSHF. The key to the results obtained is that, when GSHF control with constrained structure is applied to a periodic system, a discrete-time time-invariant, decentralized system is obtained for which control design methods are available     

Least square geometric iterative fitting method for generalized B-spline curves with two different kinds of weights

Generalized B-spline bases are generated by monotone increasing and continuous “core” functions; thus generalized B-spline curves and surfaces not only hold almost the same perfect properties which classical B-splines hold but also show more flexibility in practical applications. Geometric iterative method (also known as progressive iterative approximation method) has good adaptability and stability and is popular due to its straight geometric meaning. However, in classical geometric iterative method, the number of control points is the same as that of data points. It is not suitable when large numbers of data points need to be fitted. In order to combine the advantages of generalized B-splines with those of geometric iterative method, a fresh least square geometric iterative fitting method for generalized B-splines is given, and two different kinds of weights are also introduced. The fitting method develops a series of fitting curves by adjusting control points iteratively, and the limit curve is weighted least square fitting result to the given large data points. Detailed discussion about choosing of core functions and two kinds of weights are also given. Plentiful numerical examples are also presented to show the effectiveness of the method.     

Discrete-time control of continuous systems with approximate decentralized fixed modes

In this paper, the discrete-time control of decentralized continuous-time systems, which have approximate decentralized fixed modes, is studied. It is shown that under certain conditions, discrete-time controllers can improve the overall performance of the decentralized control system, when a linear time-invariant continuous-time controller is ineffective. In order to obtain these conditions, a quantitative measure for different types of approximate fixed modes in a decentralized system is given. In this case, it is shown that discrete-time zero-order hold (ZOH) controllers, and in particular, that generalized sampled-data hold functions (GSHF), can significantly improve the overall performance of the resultant closed-loop system. The proposed sampled-data controller is, in fact, a linear time-varying controller for the continuous-time system.     

Generalized neurofuzzy network modeling algorithms usingBezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.     

On the characterization of sampled-data controllers in the lifted domain

In this paper, we address sampled-data control problems on which the sampling and hold devices are not fixed before-hand, but are chosen as part of the design process. The idea is to apply the lifting transformation to the plant and then characterize the set of generalized sampled-data controllers, i.e., those that can be implemented as the cascade of a generalized sampler, a discrete-time controller and a generalized hold. We argue that our approach offers significant advantages over previous works, and will illustrate this by considering three applications: the input/output stabilization by generalized sampled-data controllers, the solution of the H_∞ optimal control problem and the combination of analog and digital controllers.     

Discrete-time loop transfer recovery via generalized sampled-data hold functions based compensator

Loop transfer recovery (LTR) techniques are known to enhance the input or output robustness properties of linear quadratic gaussian (LQG) designs. One restriction of the existing discrete-time LQG/LTR methods is that they can obtain arbitrarily good recovery only for minimum-phase plants. A number of researchers have attempted to devise new techniques to cope with non-minimum-phase plants and have achieved some degrees of success.^6-9 Nevertheless, their methods only work for a restricted class of non-minimum-phase systems. Here, we explore the zero placement capability of generalized sampled-data hold functions (GSHF) developed in Reference 14 and show that using the arbitrary zero placement capability of GSHF, the discretized plant can always be made minimum-phase. As a consequence, we are able to achieve discrete-time perfect recovery using a GSHF-based compensator irrespective of whether the underlying continuous-time plant is minimum-phase or not.     

求解第二类广义旅行商问题的虚顶点遗传算法

按照费用函数满足约束条件的不同,可以把广义旅行商问题(GeneralizedTravelingSalesmanProblem,简称GTSP)分为两类。目前,对GTSP解法的研究主要是面向费用函数满足三角不等式的第一类问题,而对于费用函数不满足三角不等式的第二类问题,则研究的比较少。文章针对第二类GTSP问题,提出了在广义染色体中加入虚顶点的新遗传算法。经过14个TSP问题库内的基准问题的测试表明,新算法是有效的。     

H 2-optimization and fixed poles of sampled-data systems with generalized hold functions and delay

A frequency-polynomial H ₂-optimization method for sampled-data systems with generalized hold functions and delay is proposed, which is based on the application of the concept of a parametric transfer matrix. A number of general properties of H ₂-optimal systems that must be taken into account in solving practical design problems are established.     

A set of new Chebyshev kernel functions for support vector machine pattern classification

In this study, we introduce a set of new kernel functions derived from the generalized Chebyshev polynomials. The proposed generalized Chebyshev polynomials allow us to derive different kernel functions. By using these polynomial functions, we generalize recently introduced Chebyshev kernel function for vector inputs and, as a result, we obtain a robust set of kernel functions for Support Vector Machine (SVM) classification. Thus in this study, besides clarifying how to apply the Chebyshev kernel functions on vector inputs, we also increase the generalization capability of the previously proposed Chebyshev kernels and show how to derive new kernel functions by using the generalized Chebyshev polynomials. The proposed set of kernel functions provides competitive performance when compared to all other common kernel functions on average for the simulation datasets. The results indicate that they can be used as a good alternative to other common kernel functions for SVM classification in order to obtain better accuracy. Moreover, test results show that the generalized Chebyshev kernel approaches to the minimum support vector number for classification in general.     

Generalized sample hold functions-frequency domain analysis ofrobustness, sensitivity, and intersample difficulties

Over the past 5 years, there has been substantial interest in the use of generalized sample hold functions for control. In this correspondence, the authors use a tool, which is novel in this context, namely, amplitude modulation theory. The authors employ this tool to analyze the quantitative and qualitative features of the intersample behavior in a frequency domain setting. This offers new theoretical and practical insights into the method. The authors' conclusion is that the perceived benefits come at substantial cost which makes its practical use questionable     

Associativity in combination of belief functions; a derivation of minC combination

 The nature of a contradiction (conflict) between two belief functions is investigated. Alternative ways of distributing the contradiction among nonempty subsets of frame of discernment are studied. The paper employes a new approach to understanding contradictions and introduces an original notion of potential contradiction. A method of an associative combination of generalized belief functions – minC combination and its derivation – is presented as part of the new approach. A proportionalization of generalized results is suggested as well. RID="*" ID="*" Support by Grant No. 1030803 of the GA AV ČR is acknowledged. I am grateful to Philippe Smets for a fruitful discussion on the topic.     

Control of linear systems using generalized sampled-data hold functions

This paper investigates the use of generalized sampled-data hold functions (GSHF) in the control of linear time-invariant systems. The idea of GSHF is to periodically sample the output of the system, and generate the control by means of a hold function applied to the resulting sequence. The hold function is chosen based on the dynamics of the system to be controlled. This method appears to have several advantages over dynamic controllers: it has the efficacy of state feedback without the requirement of state estimation; it provides the control system designer with substantially more freedom; and it requires few on-line computations. This paper focuses on four questions: pole assignment, specific behavior, noise sensitivity, and robustness. Among the problems solved are: simultaneous arbitrary pole assignment for a finite number of systems by a single GSHF controller, exact model matching, decoupling, and optimal noise rejection. Examples are given.     

On the extension of classical propositional logic by means of a triangular norm

In this article, we introduce a generalized extension principle by substituting a more general triangular norm T for the min intersection operator in Zadeh's extension principle. We also introduce a family of propositional logics, sup- T extension logics, obtained by the extension of classical-logical functions. A few general properties of these sup-T extension logics are derived. It is also shown that classical binary logic and the Kleene ternary logic are special cases of these logics for any choice of T, obtained by a convenient restriction of the truth domain. the very practical decomposability property of classical logic is furthermore shown to hold for the sup-min extension logic, albeit in a somewhat more limited form.     

The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

The Pythagorean fuzzy set (PFS) is characterized by two functions expressing the degree of membership and the degree of nonmembership, which square sum of them is equal or less than 1. It was proposed as a generalization of a fuzzy set to deal with indeterminate and inconsistent information. In this study, we shall present some novel Dice similarity measures of PFSs and the generalized Dice similarity measures of PFSs and indicates that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures-based multiple attribute group decision-making models with Pythagorean fuzzy information. Then, we apply the generalized Dice similarity measures between PFSs to multiple attribute group decision making. Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for selecting the desirable ERP system.     

Robustness of zero shifting via generalized sampled-data holdfunctions

In this paper we study robustness and sensitivity properties of a sampled-data feedback system with a generalized sampled-data hold function (GSHF). We argue that shifting non-minimum phase zeros using GSHF control can lead to difficulties unless the zero is outside the closed-loop bandwidth     

Similarity,topology, and uniformity

We generalize various notions of generalized metrics even further to one general concept comprising them all. For convenience, we turn around the ordering in the target domain of the generalized metrics so that we speak of similarity instead of distance. Starting from an extremely general situation without axioms, we examine which axioms or additional properties are needed to obtain useful results. For instance, we shall see that commutativity and associativity of the generalized version of addition occurring in the triangle inequality are not really needed, nor do we require a generalized version of subtraction.Each similarity space comes with its own domain of possible similarity values. Therefore, we consider non-expanding functions modulo some rescaling between different domains of similarity values. We show that non-expanding functions with locally varying rescaling functions correspond to topologically continuous functions, while non-expanding functions with a globally fixed rescaling generalize uniformly continuous functions.     

Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms

This paper analyses extensions of No-Free-Lunch (NFL) theorems to countably infinite and uncountable infinite domains and investigates the design of optimal optimization algorithms. The original NFL theorem due to Wolpert and Macready states that, for finite search domains, all search heuristics have the same performance when averaged over the uniform distribution over all possible functions. For infinite domains the extension of the concept of distribution over all possible functions involves measurability issues and stochastic process theory. For countably infinite domains, we prove that the natural extension of NFL theorems, for the current formalization of probability, does not hold, but that a weaker form of NFL does hold, by stating the existence of non-trivial distributions of fitness leading to equal performances for all search heuristics. Our main result is that for continuous domains, NFL does not hold. This free-lunch theorem is based on the formalization of the concept of random fitness functions by means of random fields.     

多输入多输出非线性系统的最小二乘支持向量机广义逆控制

针对神经网络逆控制存在的不足, 对一类模型未知且某些状态量较难测得的多输入多输出(MIMO)非线性系统, 在状态软测量函数存在的前提下, 提出一种最小二乘支持向量机(LSSVM)广义逆辨识控制策略. 通过广义逆将原被控系统转化为伪线性复合系统, 并可使其极点任意配置, 采用LSSVM代替神经网络拟合广义逆系统中的静态非线性映射. 将系统的状态量辨识与LSSVM逆模型辨识结合, 通过LSSVM训练拟合同时实现软测量功能. 最后以双电机变频调速系统为对象, 采用该控制策略进行仿真研究, 结果验证了本文算法的有效性.