面向对象与结构的系统分析与设计方法应用研究

该文在分析了面向对象方法和结构化方法的优缺点的基础上,对在系统分析与设计中综合应用两种方法进行了研究探讨,并提出了面向对象与结构的系统分析与设计方法。     

Optimal design of arch dams subjected to earthquake loading by a combination of simultaneous perturbation stochastic approximation and particle swarm algorithms

An efficient optimization procedure is introduced to find the optimal shapes of arch dams considering fluid–structure interaction subject to earthquake loading. The optimization is performed by a combination of simultaneous perturbation stochastic approximation (SPSA) and particle swarm optimization (PSO) algorithms. This serial integration of the two single methods is termed as SPSA–PSO. The operation of SPSA–PSO includes three phases. In the first phase, a preliminary optimization is accomplished using the SPSA. In the second phase, an optimal initial swarm is produced using the first phase results. In the last phase, the PSO is employed to find the optimum design using the optimal initial swarm. The numerical results demonstrate the high performance of the proposed strategy for optimal design of arch dams. The solutions obtained by the SPSA–PSO are compared with those of SPSA and PSO. It is revealed that the SPSA–PSO converges to a superior solution compared to the SPSA and PSO having a lower computation cost.     

CAD-based simulations and design of experiments for determining thrust force in drilling operations

Drilling thrust force calculations require a large amount of experimental work, which can be greatly reduced, since an extensively validated CAD-based approach, using the DRILL3D software application, has become available. DRILL3D calculates the thrust force of both the cutting areas of the tool (main edges and chisel edge) simultaneously, which means that every simulation can substitute two separate lab experiments. Nevertheless, as the number of parameters involved is increasing, the amount of the necessary simulations becomes substantial. This is the reason that led to the combined use of the DRILL3D and the design of experiments methodology, which reduces the amount of the necessary digital experiments to an impressive degree. The main factors affecting the current analysis are the tool diameter, the web to diameter ratio, the feed rate and the cutting speed used. Using an L16 Taguchi table, a function of the developed thrust force can be calculated using the response surface methodology. This statistical modeling tool employs the regression analysis to establish the relationship between various process parameters and response.     

Vibration suppression control of a flexible arm using a nonlinear observer with simultaneous perturbation stochastic approximation

The main objective in this study is the vibrational control of a one-link flexible arm system. A variable structure system (VSS) nonlinear observer has been proposed in order to reduce the oscillation when controlling the angle-of the flexible arm. The parameters of the nonlinear observer are optimized using a modified version of the simultaneous perturbation stochastic approximation (SPSA) algorithm. The SPSA algorithm is especially useful when the number of parameters to be adjusted is large, and makes it possible to estimate them simultaneously. For the vibration and position control, a model reference sliding-mode control (MR-SMC) has been proposed. The MR-SMC parameters are also optimized using a modified version of the SPSA algorithm. The simulations show that vibrational control of a one-link flexible arm system can be achieved more efficiently using our method. Therefore, by applying the MR-SMC method to a nonlinear observer, we can improve the performance in this kind of model using our proposed SPSA algorithm, and we can determine the control parameters very easy and efficiently.     

Universal parameter optimisation in games based on SPSA

Most game programs have a large number of parameters that are crucial for their performance. While tuning these parameters by hand is rather difficult, efficient and easy to use generic automatic parameter optimisation algorithms are known only for special problems such as the adjustment of the parameters of an evaluation function. The SPSA algorithm (Simultaneous Perturbation Stochastic Approximation) is a generic stochastic gradient method for optimising an objective function when an analytic expression of the gradient is not available, a frequent case in game programs. Further, SPSA in its canonical form is very easy to implement. As such, it is an attractive choice for parameter optimisation in game programs, both due to its generality and simplicity. The goal of this paper is twofold: (i) to introduce SPSA for the game programming community by putting it into a game-programming perspective, and (ii) to propose and discuss several methods that can be used to enhance the performance of SPSA. These methods include using common random numbers and antithetic variables, a combination of SPSA with RPROP, and the reuse of samples of previous performance evaluations. SPSA with the proposed enhancements was tested in some large-scale experiments on tuning the parameters of an opponent model, a policy and an evaluation function in our poker program, MCRAISE. Whilst SPSA with no enhancements failed to make progress using the allocated resources, SPSA with the enhancements proved to be competitive with other methods, including TD-learning; increasing the average payoff per game by as large as 0.19 times the size of the amount of the small bet. From the experimental study, we conclude that the use of an appropriately enhanced variant of SPSA for the optimisation of game program parameters is a viable approach, especially if no good alternative exist for the types of parameters considered. Editors: Michael Bowling · Johannes Fürnkranz · Thore Graepel · Ron Musick     

Basic Singular Spectrum Analysis and forecasting with R

Singular Spectrum Analysis (SSA) is a powerful tool of analysis and forecasting of time series. The main features of the Rssa package, which efficiently implements the SSA algorithms and methodology in R, are described. Analysis, forecasting and parameter estimation are demonstrated using case studies. These studies are supplemented with accompanying code fragments.     

Actor-critic algorithms for hierarchical Markov decision processes

We consider the problem of control of hierarchical Markov decision processes and develop a simulation based two-timescale actor-critic algorithm in a general framework. We also develop certain approximation algorithms that require less computation and satisfy a performance bound. One of the approximation algorithms is a three-timescale actor-critic algorithm while the other is a two-timescale algorithm, however, which operates in two separate stages. All our algorithms recursively update randomized policies using the simultaneous perturbation stochastic approximation (SPSA) methodology. We briefly present the convergence analysis of our algorithms. We then present numerical experiments on a problem of production planning in semiconductor fabs on which we compare the performance of all algorithms together with policy iteration. Algorithms based on certain Hadamard matrix based deterministic perturbations are found to show the best results.     

Performance analysis of model-free PID tuning of MIMO systems based on simultaneous perturbation stochastic approximation

This paper addresses the performance comparison of simultaneous perturbation stochastic approximation (SPSA) based methods for PID tuning of MIMO systems. Four typical SPSA based methods, which are one-measurement SPSA (1SPSA), two-measurement SPSA (2SPSA), Global SPSA (GSPSA) and Adaptive SPSA (ASPSA) are examined. Their performances are evaluated by extensive simulation for several controller design examples, in terms of the stability of the closed-loop system, tracking performance and computation time. In addition, the performance of the SPSA based methods are compared to the other stochastic optimization based approaches. It turns out that the GSPSA based algorithm is the most practical in terms of the stability and the tracking performance.     

Comparative study of stochastic algorithms for system optimizationbased on gradient approximations

Stochastic approximation (SA) algorithms can be used in system optimization problems for which only noisy measurements of the system are available and the gradient of the loss function is not. This type of problem can be found in adaptive control, neural network training, experimental design, stochastic optimization, and many other areas. This paper studies three types of SA algorithms in a multivariate Kiefer-Wolfowitz setting, which uses only noisy measurements of the loss function (i.e., no loss function gradient measurements). The algorithms considered are: the standard finite-difference SA (FDSA) and two accelerated algorithms, the random directions SA (RDSA) and the simultaneous-perturbation SA (SPSA). RDSA and SPSA use randomized gradient approximations based on (generally) far fewer function measurements than FDSA in each Iteration. This paper describes the asymptotic error distribution for a class of RDSA algorithms, and compares the RDSA, SPSA, and FDSA algorithms theoretically (using mean-square errors computed from asymptotic distributions) and numerically. Based on the theoretical and numerical results, SPSA is the preferable algorithm to use.     

UML及面向对象的分析与设计的研究

详细阐述了UML本身的特点和一些容易混淆的概念。对支持UML建模的各种工具和UML中的各种框图,也进行了详细的介绍。根据成、德、绵区域网络化制造系统的分析和设计经验,清晰地说明如何用UML来完成一个系统分析和设计,深入地研究了建模过程所需要用到的框图和各框图之间的关系,对面向对象的分析和设计有很强的指导性。     

AADL建模工具的设计

设计一种架构分析与设计语言(AADL)建模工具,便于用户建立AADL模型.基于模型驱动体系结构思想,在Eclipse插件开发平台上开发可扩展的插件集合AADL模型设计工具.用户根据该工具建立的嵌入式系统AADL架构模型,通过XML文档保存,分析与验证工具以第三方插件的形式集成到该工具中,提取系统的AADL架构模型.通过应用实例验证了该工具的有效性.     

Global Random Optimization by Simultaneous Perturbation Stochastic Approximation

We examine the theoretical and numerical global convergence properties of a certain ldquogradient freerdquo stochastic approximation algorithm called the ldquosimultaneous perturbation stochastic approximation (SPSA)rdquo that has performed well in complex optimization problems. We establish two theorems on the global convergence of SPSA, the first involving the well-known method of injected noise. The second theorem establishes conditions under which ldquobasicrdquo SPSA without injected noise can achieve convergence in probability to a global optimum, a result with important practical benefits.     

A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Over recent years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as producing more customized products in a shorter time. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a concern for high performance. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits of Industry 4.0 technologies. ZDM imposes the rule that any event in the production process should have a counter-action to mitigate it. In light of this, the current research developed a methodology the manufacturer can use to correctly select or design appropriate ZDM strategies and equipment to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using a dynamic scheduling tool with specific data sets to develop a digital twin (DT). The data sets are created using the Taguchi design of experiments methodology. The DT model is created for use in predicting the results of the developed scheduling tool without actually using said tool. Using the DT, multiple ZDM parameter-combination sets can be created and plugged into the model. This process generates ZDM performance maps that show the effect of each ZDM strategy at each manufacturing stage under different control parameters. These maps are intended to provide information for comparing different ZDM-oriented equipment to help manufacturers reach a final decision on correct and efficient ZDM implementation or to assist in the design phase of a ZDM strategy implementation.     

Electromagnetism-like algorithms for optimized tool path planning in 5-axis flank machining

Optimization of tool path planning using metaheuristic algorithms such as ant colony systems (ACS) and particle swarm optimization (PSO) provides a feasible approach to reduce geometrical machining errors in 5-axis flank machining of ruled surfaces. The optimal solutions of these algorithms exhibit an unsatisfactory quality in a high-dimensional search space. In this study, various algorithms derived from the electromagnetism-like mechanism (EM) were applied. The test results of representative surfaces showed that all EM-based methods yield more effective optimal solutions than does PSO, despite a longer search time. A new EM-MSS (electromagnetism-like mechanism with move solution screening) algorithm produces the most favorable results by ensuring the continuous improvement of new searches. Incorporating an SPSA (simultaneous perturbation stochastic approximation) technique further improves the search results with effective initial solutions. This work enhances the practical values of tool path planning by providing a satisfactory machining quality.     

Experimental design in soap manufacturing for optimization of fuzzified process capability index

In manufacturing industries, the quality of a product depends on the combined effect of multiple input variables working singly or together and therefore attention has been given on process capability indices to shift from single to multivariate domain. In case of multivariable domain the capability to incorporate uncertainties at the time of decision making becomes necessary. Fuzzy system is introduced to take care of this requirement. In this article the process parameters of soap manufacturing industries have been analyzed. The process capability is determined using Fuzzy Inference System rule editor based on a set of justified if then statements as applicable for the process. The data has been collected in linguistic form to derive its process capability, using a set of justified rules and the effect of each factor has been determined using Design of Experiments (DoE) and analysis of variance technique (ANOVA) for improving the soap quality from perspective of its softness. This article ventures to propose a new methodology by integrating Fuzzy with DoE providing better result followed by DoE and Fuzzy Inference system in isolation.     

Multidisciplinary design optimization of a small solid propellant launch vehicle using system sensitivity analysis

Multidisciplinary design optimization approaches have significant effects on aerospace vehicle design methodology. In designing next generation of space launch systems, MDO processes will face new and greater challenges. This study develops a system sensitivity analysis method to optimize multidisciplinary design of a two-stage small solid propellant launch vehicle. Suitable design variables, technological, and functional constraints are considered. Appropriate combinations of disciplines such as propulsion, weight, geometry, and trajectory simulation are used. A generalized sensitivity equation is developed and solved. These results are basis for optimization. Comparison of the developed approach with gradient optimization methods reveals that developed approach requires less computation time.     

珠三角地区多机场系统航班时刻的仿真优化

珠三角地区机场分布稠密、空域紧张、航班延误率高。为降低该地区的航班延误,提出以延误分层模型为优化目标,采用空域机场仿真模型SIMMOD对珠三角地区多机场系统仿真建模和有约束限制的并行扰动随机近似（Simultaneous Perturbation Stochastic Approximation,SPSA）优化算法相结合的方法,对珠三角地区多机场系统的航班时刻进行仿真优化研究。采用非光滑反馈对SPSA算法进行了改进。仿真结果表明采用这种仿真优化方法生成的新航班时刻,可有效降低总延误时间。研究对从整体上减少珠三角地区多机场系统的延误成本具有重要价值。     

A diagonal recurrent neural network-based hybrid direct adaptiveSPSA control system

A direct adaptive simultaneous perturbation stochastic approximation (DA SPSA) control system with a diagonal recurrent neural network (DRNN) controller is proposed. The DA SPSA control system with DRNN has simpler architecture and parameter vector size that is smaller than a feedforward neural network (FNN) controller. The simulation results show that it has a faster convergence rate than FNN controller. It results in a steady-state error and is sensitive to SPSA coefficients and termination condition. For trajectory control purpose, a hybrid control system scheme with a conventional PID controller is proposed     

Simultaneous Perturbation Stochastic Approximation with Norm‐Limited Update Vector

This paper addresses the convergence of simultaneous perturbation stochastic approximation (SPSA) with a norm‐limited update vector. We first illustrate an unstable solution of the standard SPSA algorithm which motivates the consideration of a modified version, where the norm of the update vector is limited to a certain value. Next, a result on the almost‐sure convergence is presented by reducing the modified algorithm into the standard SPSA algorithm and restricting the probability distribution for the perturbation to a Bernoulli distribution. Finally, we apply the modified algorithm to a system identification problem to demonstrate its performance.