Call for papers Journal of Control Theory and Applications Special issue on Approximate dynamic programming and reinforcement learning

Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain and nonconvex environments over time.

Parallel Control for Optimal Tracking via Adaptive Dynamic Programming

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.

Approach for uncertain multi-objective programming problems with correlated objective functions under C_(EV) criterion

An uncertain multi-objective programming problem is a special type of mathematical multi-objective programming involving uncertain variables. This type of problem is important because there are several uncertain variables in real-world problems.Therefore, research on the uncertain multi-objective programming problem is highly relevant, particularly those problems whose objective functions are correlated. In this paper, an approach that solves an uncertain multi-objective programming problem under the expected-variance value criterion is proposed. First, we define the basic framework of the approach and review concepts such as a Pareto efficient solution and expected-variance value criterion using an order relation between various uncertain variables.Second, the uncertain multi-objective problem is converted into an uncertain single-objective programming problem via a linear weighted method or ideal point method. Then the problem is transformed into a deterministic single objective programming problem under the expected-variance value criterion. Third, four lemmas and two theorems are proved to illustrate that the optimal solution of the deterministic single-objective programming problem is an efficient solution to the original uncertainty problem. Finally, two numerical examples are presented to validate the effectiveness of the proposed approach.

New approach for uncertain random multi-objective programming problems based on C_(ESD) criterion

To overcome the defects that the traditional ap-proach for multi-objective programming under uncertain ran-dom environment(URMOP)neglects the randomness and uncer-tainty of the problem and the volatility of the results,a new ap-proach is proposed based on expected value-standard devi-ation value criterion(C_(ESD) criterion).Firstly,the effective solution to the URMOP problem is defined;then,by applying sequence relationship between the uncertain random variables,the UR-MOP problem is transformed into a single-objective program-ming(SOP)under uncertain random environment(URSOP),which are transformed into a deterministic counterpart based on the C_(ESD) criterion.Then the validity of the new approach is proved that the optimal solution to the SOP problem is also effi-cient for the URMOP problem;finally,a numerical example and a case application are presented to show the effectiveness of the new approach.

Assembling Paradigms of Programming in Software Engeneering

Assembling paradigms programming are based on the reuses in any programming language (PL) with the passport data of their settings in WSDL. The method of assembling is formal and secures co-operation of the different reuses (module, object, component, service and so on) being developed. A formal means of these paradigms creation with help of interfaces is presented. Interface IDL (Stub, Skeleton) is containing data and operations for transmission data to other standard elements linked and describes in the standard language IDL. Assembling will be realized by integration of reuses elements in these paradigms on the instrumental-technological complex (ITC).

AN IMPROVEMENT ON UNCONSTRAINED CONVEX PROGRAMMING APPROACH TO LINEAR PROGRAMMING

For satate form linear gram as Fang and sao deined and approach which would find an optimal solution by solving an anconstrained convex dual programming.Thedual was construcied by applying an emropic peturbation and a simple Inequality Inz【z-1 for z】0n,In this paper,we suggest than a paperbation functiontake the place of Inx such that the new approdt has good numerical stability andhas all properties of the original

An Approach to Solve a Possibilistic Linear Programming Problem

The objective of the paper is to deal with a kind of possibilistic linear programming (PLP) problem involving multiple objectives of conflicting nature. In particular, we have considered a multi objective linear programming (MOLP) problem whose objective is to simultaneously minimize cost and maximize profit in a supply chain where cost and profit coefficients, and related parameters such as available supply, forecast demand and budget are fuzzy with trapezoidal fuzzy numbers. An example is given to illustrate the strategy used to solve the aforesaid PLP problem.

Integer Programming Model for Maximum Clique in Graph

The maximum clique or maximum independent set of graph is a classical problem in graph theory. Com- bined with Boolean algebra and integer programming, two integer programming models for maximum clique problem, which improve the old results were designed in this paper. Then, the programming model for maximum independent set is a corollary of the main results. These two models can be easily applied to computer algorithm and software, and suitable for graphs of any scale. Finally the models are presented as Lingo algorithms, verified and compared by sev- eral examples.

The Sliding Gradient Algorithm for Linear Programming

The existence of strongly polynomial algorithm for linear programming (LP) has been widely sought after for decades. Recently, a new approach called Gravity Sliding algorithm [1] has emerged. It is a gradient descending method whereby the descending trajectory slides along the inner surfaces of a polyhedron until it reaches the optimal point. In R3, a water droplet pulled by gravitational force traces the shortest path to descend to the lowest point. As the Gravity Sliding algorithm emulates the water droplet trajectory, it exhibits strongly polynomial behavior in R3. We believe that it could be a strongly polynomial algorithm for linear programming in Rn too. In fact, our algorithm can solve the Klee-Minty deformed cube problem in only two iterations, irrespective of the dimension of the cube. The core of gravity sliding algorithm is how to calculate the projection of the gravity vector g onto the intersection of a group of facets, which is disclosed in the same paper [1]. In this paper, we introduce a more efficient method to compute the gradient projections on complementary facets, and rename it the Sliding Gradient algorithm under the new projection calculation.

An Uncertain Programming Model for Competitive Logistics Distribution Center Location Problem

We employ uncertain programming to investigate the competitive logistics distribution center location problem in uncertain environment, in which the demands of customers and the setup costs of new distribution centers are uncertain variables. This research was studied with the assumption that customers patronize the nearest distribution center to satisfy their full demands. Within the framework of uncertainty theory, we construct the expected value model to maximize the expected profit of the new distribution center. In order to seek for the optimal solution, this model can be transformed into its deterministic form by taking advantage of the operational law of uncertain variables. Then we can use mathematical software to obtain the optimal location. In addition, a numerical example is presented to illustrate the effectiveness of the presented model.

A Dynamic Programming Approach for the Max-Min Cycle Packing Problem in Even Graphs

Let be an undirected graph. The maximum cycle packing problem in G then is to find a collection of edge-disjoint cycles Ciin G such that s is maximum. In general, the maximum cycle packing problem is NP-hard. In this paper, it is shown for even graphs that if such a collection satisfies the condition that it minimizes the quantityon the set of all edge-disjoint cycle collections, then it is a maximum cycle packing. The paper shows that the determination of such a packing can be solved by a dynamic programming approach. For its solution, an-shortest path procedure on an appropriate acyclic networkis presented. It uses a particular monotonous node potential.

Research on Regulating and Controlling Homogenization of Entertainment Programs--Based on Game Theory

With the prevailing and popular entertainment programs, it brought huge benefits to TV stations, but at the same time caused a variety problems of homogenization. The paper analyzes the causes of the entertainment programs, the root of homogenization, building game theory models between TV stations, TV station and audience to regulate and control the issue of homogeneity about entertainment programs.

AN ALGORITHM FOR L-SHAPED CONVEX PROGRAMMING

In this paper we study L-shaped convex programming. An algorithm for it is given. The result of computation shows that the algorithm is effective. The algorithm can be applied to two stage problem of stochastic convex programming.

企业如何通过数字化转型实现颠覆性创新?——基于潍柴集团的案例研究

数字经济时代促使企业进入数字化转型过程中,企业数字化转型离不开数字技术的发展与数字产物的嵌入,在二者的催化下多端颠覆性创新成为企业转型新常态,而数字化转型如何赋能制造业企业颠覆性创新形成数字化颠覆性创新以把握数字时代契机,打造企业新型竞争优势是可拓展的研究问题。本文以潍柴集团为例,依托于颠覆性创新理论,通过程序化扎根理论分析对象,对制造业企业实现数字化颠覆性创新的过程机制以及形成路径进行探究。研究发现,企业进行数字化颠覆性创新要经历驱动、转型以及多元三个阶段。驱动阶段,企业受内外驱动因素影响,启动数字化颠覆性创新;转型阶段,企业以生产模式转变与组织结构演化为行动路径,通过对企业内外的数字化实现企业的数字化颠覆性创新;多元阶段,通过实现商业生态系统的数字化颠覆性创新进行创新外延,形成数字商业生态系统模式,完成颠覆性创新转型。

Snyder希望理论+支持性照护综合护理方案在超声甲状腺结节患者中的应用效果

目的探究Snyder希望理论+支持性照护综合护理方案在超声甲状腺结节患者中的应用效果。方法选择2023年4月至9月北京中医药大学东直门医院80例超声甲状腺结节患者,采用随机数字表法将其分成对照组和观察组,各40例。对照组采用常规护理干预,观察组采用Snyder希望理论+支持性照护综合护理干预,干预时间为2周。比较两组干预前后中文版Herth希望量表(HHI)评分、家庭关怀度指数(APGAR)评分及自我管理能力评分。结果干预后,两组HHI评分高于干预前,且观察组高于对照组(P<0.05)。两组APGAR评分高于干预前,且观察组高于对照组(P<0.05)。两组自我管理能力评分高于干预前,且观察组高于对照组(P<0.05)。结论Snyder希望理论+支持性照护综合护理方案在超声甲状腺结节患者中的应用效果显著,可以提升患者希望水平、家庭功能与自我管理能力,值得在临床推广应用。

基于案例引导的可靠性理论教学模式探析

常规可靠性理论教学存在重视公式推导而轻视案例分析的现状,课程教学效果不佳。文章探索用案例教学法讲授该课程知识,着重强调案例教学理念与编程实践促学的重要性。结合4个典型工程案例,如悬臂梁、海堤、基桩、板桩墙等构筑物,说明可靠性理论教学实施的步骤与方法。每个案例各有侧重,由浅入深地引出重要知识点,通过编程实践引导学生进行实例求解。基于案例启发式的教学模式可激发学生探求可靠性理论的学习热情,从而实现与专业问题相互衔接。

国际中文教育项目式教学评价:从理论到实践的路径

在数字化转型的背景下,国际中文教育的形式正经历着教学理念、教学形态和业态、人才结构以及评价方式的变革。本文指出项目式教学是推动国际中文教育变革的潜在力量。同时,本文强调评价体系在保证项目式教学效果和质量、指导教学设计与实施中的关键作用,并指出现有评价方法存在过于关注阶段性语言教学成果,忽略社会文化交互的弊端。基于成果导向教育理论和社会教育学理论,本文提出一个综合性的评价体系,不仅注重学生的汉语学习成就和个人发展,也重视中文项目对社会的贡献。本文分析评价体系的构建原则、标准、方法和程序,旨在为国际中文教育的项目式教学提供一个全面的评价工具,以促进教学质量和效果的持续提升。

中国特色教育技术学基础理论研究框架与运用示例--基于教育哲学和程序化扎根理论

基础理论研究是任何学科发展的命脉是学科之基。然而,在教育数字化转型和当前主流教育实证研究范式下,我国教育技术学对本土化的基础理论研究的关注度还远不够。基础理论是我国教育技术学的根本,建构中国特色教育技术学基础理论体系尤为重要。当前,中国特色教育技术学基础理论体系尚未形成很大原因是缺少方法论指导。基于此,文章从方法论的层面探讨如何开展中国特色教育技术学基础理论研究框架,首先,从教育哲学视角对基础理论体系分类;其次,基于教育哲学的运思方式和程序化扎根理论方法,建构基础理论的研究范畴;最后,举例说明如何利用该框架开展中国特色教育技术学基础理论的研究,以期为我国教育技术学基础理论研究工作尽绵薄之力。

不同驱动系统下纯电动汽车关键性能对比研究

为比较纯电动汽车不同驱动系统的关键性能,基于同一整车参数和某公司提供的可变绕组永磁同步电机试验数据,对纯电动汽车电机驱动系统开展了相关研究。基于精英保留遗传算法和动态规划理论,对单挡、两挡电控机械式自动变速器驱动系统的速比进行了设计优化。采用了精英保留遗传算法和动态规划理论对系统速比进行设计优化,并对可变绕组永磁同步电机绕组切换过程进行了动力性和经济性设计。仿真结果表明,在动力性上,两挡自动变速器驱动系统的加速性能最优;在经济性上,可变绕组永磁同步电机驱动系统的百公里能耗最小,单挡自动变速器驱动系统的动力性和经济性表现最不理想。

基于多异构属性和不完全权重信息的案例检索方法

针对多异构属性和不完全权重信息的案例检索问题,提出了一种基于后悔理论的案例检索方法。通过定义基于属性相似度和后悔理论(RT)的感知效用函数,并基于线性规划技术的多维偏好分析(LINMAP),构建了确定属性权重的数学规划模型。在此基础上,计算感知效用,并确定一组类似的历史案例,计算相似历史案例的综合效用,得到相似历史案例的排序,从而获得最适合的历史案例。最后,以瓦斯爆炸案例验证该方法的可行性与有效性。结果表明:该方法可以在计算案例相似性和综合效用时提供更客观、准确的结果。